# EFFICIENCY OF DECISION TREES IN PREDICTING STUDENT S ACADEMIC PERFORMANCE

Save this PDF as:

Size: px
Start display at page:

Download "EFFICIENCY OF DECISION TREES IN PREDICTING STUDENT S ACADEMIC PERFORMANCE"

## Transcription

1 EFFICIENCY OF DECISION TREES IN PREDICTING STUDENT S ACADEMIC PERFORMANCE S. Anupama Kumar 1 and Dr. Vijayalakshmi M.N 2 1 Research Scholar, PRIST University, 1 Assistant Professor, Dept of M.C.A. 2 Associate Professor, Dept of MCA 1,2 R.V.College of Engineering, Bangalore, India. 1 2 ABSTRACT Educational data mining is used to study the data available in the educational field and bring out the hidden knowledge from it. Classification methods like decision trees, rule mining, Bayesian network etc can be applied on the educational data for predicting the students behavior, performance in examination etc. This prediction will help the tutors to identify the weak students and help them to score better marks. The C4.5 decision tree algorithm is applied on student s internal assessment data to predict their performance in the final exam. The outcome of the decision tree predicted the number of students who are likely to fail or pass. The result is given to the tutor and steps were taken to improve the performance of the students who were predicted to fail. After the declaration of the results in the final examination the marks obtained by the students are fed into the system and the results were analyzed. The comparative analysis of the results states that the prediction has helped the weaker students to improve and brought out betterment in the result. To analyse the accuracy of the algorithm, it is compared with ID3 algorithm and found to be more efficient in terms of the accurately predicting the outcome of the student and time taken to derive the tree. KEYWORDS Assessment, Prediction, Educational data mining, Decision tree, C4.5algorithm, ID3 algorithm 1. INTRODUCTION Data mining concepts and methods can be applied in various fields like marketing, stock market, real estate, customer relationship management, engineering, medicine, web mining etc. Educational data mining is a new emerging technique of data mining that can be applied on the data related to the field of education. EDM is the process of transforming raw data compiled by education systems in useful information that could be used to take informed decisions and answer research questions [1].The various techniques of data mining like classification, clustering and rule mining can be applied to bring out various hidden knowledge from the educational data. D.C. Wyld, et al. (Eds): CCSEA 2011, CS & IT 02, pp , CS & IT-CSCP 2011 DOI: /csit

2 336 Computer Science & Information Technology (CS & IT) Prediction can be classified into: classification, regression, and density estimation. In classification, the predicted variable is a binary or categorical variable. Some popular classification methods include decision trees, logistic regression and support vector machines. In regression, the predicted variable is a continuous variable. Some popular regression methods within educational data mining include linear regression, neural networks, and support vector machine regression. Classification techniques like decision trees, Bayesian networks etc can be used to predict the student s behavior in an educational environment, his interest towards a subject or his outcome in the examination. Examination plays a vital role in any student s life. The marks obtained by the student in the examination decide his future. Therefore it becomes essential for any tutor to predict whether the student will pass or fail in the examination. If the prediction says that a student tends to fail in the examination prior to the examination then extra efforts can be taken to improve his studies and help him to pass the examination. This paper is an extension of [13] where the result of the students of I semester MCA are predicted depending upon their performance in the internal examination. We have used C4.5 (J48 in WEKA) to do the prediction analysis. The outcome of the internal marks is used in this paper for finding the efficiency of the algorithm towards educational data and the accuracy of predicting the result. This paper analyses the accuracy of the algorithm in the following ways Comparing the result of the tree with the original marks obtained by the student in the university examination Comparing C4.5 algorithm with ID3 algorithm in terms of the efficiency in building the tree and time taken to build the tree. The paper is divided into the following sections. Section II describes the background investigation, Section III describes the data collection, IV explain the methodology, V explains the findings of the research and VI consists of the conclusion and future Enhancements. 2. BACKGROUND INVESTIGATION Predicting the academic outcome of a student needs lots of parameters to be considered. Data pertaining to student s background knowledge about the subject, the proficiency in attending a question, the ability to complete the examination in time etc will also play a role in predicting his performance. M.N. Quadri and Dr. N.V. Kalyankar [3] have predicted student s academic performance using the CGPA grade system where the data set comprised of the students gender, his parental education details, his financial background etc. In [2] the author has explored the various variables to predict the students who are at risk to fail in the exam. The solution strongly suggests that the previous academic result strongly plays a major role in predicting their current outcome. In accordance with [13], the marks obtained by the students during the internal examination will play a vital role in predicting the outcome of the student in the main examination. The internal marks for the subjects MCA11, MCA12, MCA13, MCA14, MCA15 for a maximum of 100 marks and a result of Pass/Fail depending upon a minimum of 50 marks from each subject is fed as input and a decision tree is obtained using C4.5 (J48 in WEKA).The

3 Computer Science & Information Technology (CS & IT) 337 output should compared with the original marks received and result obtained by the student in the university examination. 3. DATA COLLECTION The internal marks obtained by the students of I semester M.C.A has been considered as a source of data in [13] and a decision tree was drawn using the same. A slight modification has been done in defining the nominal values for the purpose of analyzing the accuracy in this paper. Here the nominal values have been categorized as (0_44) where the students are predicted as Fail, (45_54) where the students are considered to be on border line where they may pass or fail and (54_100) where the students are sure to pass. The results of I semester MCA declared by the university is the major source of data in this paper. The declared result consists of a university seat no in the alphanumeric form, which is the unique identifier and marks obtained (internal marks obtained out of 50, external marks obtained out of 100, total out of 150) in five subjects in the form of integers and a result field (containing pass/fail) in the form of string values. Among these data, the internal marks obtained by the student are already used in [paper] and a decision tree is obtained accordingly. For the purpose of research, the external marks (obtained out of 100) are considered. The marks are converted into nominal values according to the following condition: 1.(0_39) indicates a fail in the result of the student 2.(40_100) indicates a pass in the result of the student. The obtained data is preprocessed according to the need of the system. The unique identifier is removed and the integer values are then converted into nominal values and stored in the.csv format. It is then converted into the.arff format so that it is accessible in WEKA 4. METHODOLOGY A decision tree depicts rules for dividing data into groups. J48 builds decision trees from a set of training data in the same way as ID3, using the concept of information entropy. The training data is a set S = s 1,s 2,... of already classified samples. Each sample s i = x 1,x 2,... is a vector where x 1,x 2,... represent attributes or features of the sample. The training data is augmented with a vector C = c 1,c 2,... where c 1,c 2,... represent the class to which each sample belongs. At each node of the tree, J48 chooses one attribute of the data that most effectively splits its set of samples into subsets enriched in one class or the other. Its criterion is the normalized information gain (difference in entropy) that results from choosing an attribute for splitting the data. The attribute with the highest normalized information gain is chosen to make the decision. The J48 algorithm then recurs on the smaller sub lists Implementation of J48 Algorithm on Internal Marks The internal marks obtained by the students of I semester MCA has been used as a source of data in paper [13] so as to predict the outcome of the student in the university exam. However a slight

4 338 Computer Science & Information Technology (CS & IT) modification has been done in the same data for a better prediction. The resultant tree obtained from the data collected in the form of internal marks is given below: Decision tree 1: Tree obtained from internal marks The following observations can be made using the above mentioned decision tree: 1. Out of the 5 subject attributes, MCA11 was not considered in the tree, because the instance corresponding the result pass was very low. 2. The other attributes were combined to form a pruned tree. 3. Instances of MCA13 and MCA14 share almost equal number of failure students out of which MCA 14 has been considered as the root of the tree since the subject was holding maximum number of students in the range of (0_44). Beyond this MCA12 with next less number of failures has been taken a leaf and so on. 4. The total number of instances considered for deriving the tree is 117. The main aim for deriving such a tree is to improve the performance of the students and bring out better results from them. The above derived predictions are given to the tutors and are advised to give extra coaching to the students who were in the category of Pass/Fail and Fail Implementation of J48 Algorithm on External Marks The accuracy of the above result is now compared with the original result declared by the university in the month of March 11. The original result is then converted to the nominal form and a decision tree is drawn using the WEKA J48 algorithm. The decision tree obtained from the data is given below

5 Computer Science & Information Technology (CS & IT) 339 Decision tree 2: Tree obtained from External marks From the tree it is clear that there is a change in the result obtained by the student in the university examination. The following observations are made from the tree: 1. The subject MCA12 was not considered to form a tree since the number of failures was very less in the subject. 2. The subject MCA13 which has been considered as root, it has got three distinct leaf nodes where the node has depicted the student was absent for the examination. Therefore it is clear that the system is accepting a string value also. 3. The subject with more failures is taken in the root and the leaves constitute the failures less than the root. 4.3 Comparison of Prediction analysis of Internal and External Marks From the results obtained from the J48 algorithm, table 1 gives an overview of the prediction analysis made using the internal marks and the original result obtained by the students Result Obtained from Internal Marks Result Obtained from External Marks Total No. of Instances Instances Classified Correctly Instances classified Incorrectly Table 1: Comparison of Insances for internal and external marks

6 340 Computer Science & Information Technology (CS & IT) The algorithm has classified the students as pass/fail for both the correct and incorrect instances. In the case of internal marks, out of the 117 instances, 104 instances are classified as correct and 13 instances have been incorrectly classified. The table 2 describes the confusion matrix achieved through the instances. No. of Instances predicted Pass No. of Instances predicted Pass/Fail No. of Instances predicted Fail Table2: Confusion Matrix obtained for Internal Marks In case of External marks students cannot fall under the category of pass/fail since the result is declared by the university and it is compared with the prediction made. From the table 3 it is clear that out of the 104 correctly identified instances, 65 have been predicted as pass, 29 instances as fail and 23 instances can either be pass or fail. These instances are practically important and the tutors are advised to concentrate more on the Pass/fail and fail instances. Pass Fail Table 3: Confusion Matrix obtained for External Marks From table 3it is clear that out of 107 correctly classified instances, 102 students have passed and 5 students have failed. Out of 9 incorrect instances 1student passed and 5 students failed. Out of the incorrect instances one instance belongs to the student who has been marked ab ie ABSENT in the examination. To analyze the accuracy of the algorithm, the results obtained from both the internal and external marks are compared. The following inferences are made from the results obtained: 1. The students who have been predicted to be passing have been declared pass in the university exam also. 2. The students who were predicted to be pass/fail in the decision tree 1 were declared pass in the university exam. 3. Out of the 28 students predicted to be Fail, 13 students have actually failed and 15 other students have improved their studies and passed in the examination. From the above inferences it is clear that the prediction algorithm has helped the tutors to improve the performance of the students.

7 Computer Science & Information Technology (CS & IT) COMPARISON OF J48 AND ID3 ALGORITHM The main aim of the prediction analysis is to improve the academic performance of the students. The J48 prediction algorithm is analyzed using the following methods. 1. The accuracy of the algorithm is measured using the comparison of the internal and external marks obtained by the students in the university examination. 2. The efficiency of the algorithm is measured by comparing the J48 algorithm with ID3 algorithm. The data received from the university result is fed into the ID3 algorithm for analyzing the efficiency of the J48 algorithm. The algorithm is analysed in the following terms: 1. The number of instances predicted as Pass/Fail 2. The time taken to derive the tree The confusion matrix obtained by the ID3 algorithm is given below: Pass Instances classified as Fail Table 4: Confusion Matrix obtained for External Marks from ID3 By keeping all the instances common, the above table clearly specifies that the number of instances declared pass is equal in both the algorithms and the number of instances declared fail is not classified accurately. The data differes by 2 students out of which one is marked absent and the other one is unpredictable. Therefore it is clear that J48 algorithm is more accurate than ID3 algorithm. Table 4 gives the comparative analysis of both the algorithms Algorithm ID3 J48(C4.5) Pass Fail Time Taken 0.02 seconds 0 seconds Table 5: Comparison of ID3 and J48 algorithms

8 342 Computer Science & Information Technology (CS & IT) 6. CONCLUSION The various data mining techniques can be effectively implemented on educational data. From the above results it is clear that classification techniques can be applied on educational data for predicting the student s outcome and improve their results. The efficiency of various decision tree algorithms can be analyzed based on their accuracy and time taken to derive the tree. The predictions obtained from the system have helped the tutor to identify the weak students and improve their Performance. The analysis of the result declared from the university is a proof for the same. Since the application of data mining brings a lot of advantages in higher learning institution, these techniques can be applied in the other areas of education to optimize the resources, to predict the retainment of faculties in the institution, to predict the number of students who are likely to get a placement, to predict the feed back of the tutor etc. REFERENCES [1] Cecily Heiner, Ryan Baker y Kalina Yacef, -Proceedings of the Workshop on Educational Data Mining at the 8th International Conference on Intelligent Tutoring Systems Jhongli, Taiwan.,2006. [2] Zlatko J. Kovačić, John Steven Green, Predictive working tool for early identification of at risk students, Newzealand [3] M. N. Quadri1 and Dr. N.V. Kalyanka- Drop Out Feature of Student Data for Academic Performance Using Decision Tree, Global Journal of Computer Science and Technology Vol. 10 Issue 2 (Ver 1.0), April [4] Cristóbal Romero and etel, Computer Science Department, Córdoba University, Spain, Data Mining Algorithms to Classify Students [5] Zlatko J. Kovačić, Associate Professor, John Steven Green, Senior Lecturer, School of Information and Social Sciences, Open Polytechnic, NewZealand Predictive working tool for early identification of at risk students, published in Creative Commons 3.0 New Zealand Attribution Noncommercial Share Alike Licence (BY-NC-SA). [6] E.Chandra and K.Nandhini, Predicting Student Performance using Classification Techniques, Proceedings of SPIT-IEEE Colloquium and International Conference, Mumbai, India,p.no [7] S. B. Kotsiantis and etel,. Efficiency of machine learning techniques in predicting students performance in distance learning systems,proc, Recent advances in mechanics and related fields,p.no [8]. Mykola Pechenizkiy and etel, Mining the Student Assessment Data: Lessons Drawn from a Small Scale Case Study [9] S.Anupama Kumar, Dr.M.N.Vijayalakshmi, A Novel Approach in Data Mining Techniques for Educational Data, Proc rd International Conference on Machine Learning and Computing (ICMLC 2011), Singapore, 26 th -28 th Feb 2011,pp V [10]. R. Messeguer and et al, Department of Computer Architecture, Technical University of Catalonia, Spain, Group Prediction in Collaborative Learning.

9 Computer Science & Information Technology (CS & IT) 343 [11] Alaa el-halees,mining students data to analyze learning behavior:,a case study, Department of Computer Science, Islamic University of Gaza P.O.Box 108 Gaza, Palestine [12] Dr.Varun Kumar, Anupama Chanda, An Empirical Study of the Applications of Data Mining Techniques in Higher Education, (IJACSA) International Journal of Advanced Computer Science and Applications,Vol. 2, No.3, March 2011 [13] S.Anupama Kumar, Dr.Vijayalakshmi M.N.,"Prediction of the students recital using classification Technique ", IFRSA s International journal of computing (IIJC), Volume 1, Issue 3 July 2011,pp [14] Shaeela Ayesha and et al, Data Mining Model for Higher Education System, European Journal of Scientific Research, Vol.43 No.1 (2010), pp Authors Profile Ms.S.Anupama Kumar has 12 years of teaching experience. She has completed her Master of Philosophy from Alagappa University and her Masters from Bharathidasan University. She has published research papers in national and international conferences. She also has a publication in internal journal to her credit. Her research interests are in the area of data mining and artificial intelligence. She is a member of IAENG and IACSIT. Dr. Vijayalakshmi M.N. had completed her PhD from Mother Teresa Women s university, Kodaikanal in She has 12 years of teaching experience and 5 years of Research experience. She is a recognised research guide in VTU and Prist University. She has published many research papers in the national and international conferences and journals. She has got many research projects to her credit funded by different agencies. Her research interests are Pattern recognition, data mining, neural networks, Image Processing. She is a life member of ISTE, CSI, IACSIT.

### Edifice an Educational Framework using Educational Data Mining and Visual Analytics

I.J. Education and Management Engineering, 2016, 2, 24-30 Published Online March 2016 in MECS (http://www.mecs-press.net) DOI: 10.5815/ijeme.2016.02.03 Available online at http://www.mecs-press.net/ijeme

### Identifying At-Risk Students Using Machine Learning Techniques: A Case Study with IS 100

Identifying At-Risk Students Using Machine Learning Techniques: A Case Study with IS 100 Erkan Er Abstract In this paper, a model for predicting students performance levels is proposed which employs three

### Predicting Students Final GPA Using Decision Trees: A Case Study

Predicting Students Final GPA Using Decision Trees: A Case Study Mashael A. Al-Barrak and Muna Al-Razgan Abstract Educational data mining is the process of applying data mining tools and techniques to

### BOOSTING - A METHOD FOR IMPROVING THE ACCURACY OF PREDICTIVE MODEL

The Fifth International Conference on e-learning (elearning-2014), 22-23 September 2014, Belgrade, Serbia BOOSTING - A METHOD FOR IMPROVING THE ACCURACY OF PREDICTIVE MODEL SNJEŽANA MILINKOVIĆ University

### Predicting Student Performance by Using Data Mining Methods for Classification

BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 13, No 1 Sofia 2013 Print ISSN: 1311-9702; Online ISSN: 1314-4081 DOI: 10.2478/cait-2013-0006 Predicting Student Performance

### First Semester Computer Science Students Academic Performances Analysis by Using Data Mining Classification Algorithms

First Semester Computer Science Students Academic Performances Analysis by Using Data Mining Classification Algorithms Azwa Abdul Aziz, Nor Hafieza IsmailandFadhilah Ahmad Faculty Informatics & Computing

### Data quality in Accounting Information Systems

Data quality in Accounting Information Systems Comparing Several Data Mining Techniques Erjon Zoto Department of Statistics and Applied Informatics Faculty of Economy, University of Tirana Tirana, Albania

### DECISION TREE INDUCTION FOR FINANCIAL FRAUD DETECTION USING ENSEMBLE LEARNING TECHNIQUES

DECISION TREE INDUCTION FOR FINANCIAL FRAUD DETECTION USING ENSEMBLE LEARNING TECHNIQUES Vijayalakshmi Mahanra Rao 1, Yashwant Prasad Singh 2 Multimedia University, Cyberjaya, MALAYSIA 1 lakshmi.mahanra@gmail.com

### Decision Trees for Mining Data Streams Based on the Gaussian Approximation

International Journal of Computer Sciences and Engineering Open Access Review Paper Volume-4, Issue-3 E-ISSN: 2347-2693 Decision Trees for Mining Data Streams Based on the Gaussian Approximation S.Babu

### Keywords data mining, prediction techniques, decision making.

Volume 5, Issue 4, April 2015 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com Analysis of Datamining

### Empirical Study of Decision Tree and Artificial Neural Network Algorithm for Mining Educational Database

Empirical Study of Decision Tree and Artificial Neural Network Algorithm for Mining Educational Database A.O. Osofisan 1, O.O. Adeyemo 2 & S.T. Oluwasusi 3 Department of Computer Science, University of

### Mining Educational Data to Improve Students Performance: A Case Study

Mining Educational Data to Improve Students Performance: A Case Study Mohammed M. Abu Tair, Alaa M. El-Halees Faculty of Information Technology Islamic University of Gaza Gaza, Palestine ABSTRACT Educational

### 2. IMPLEMENTATION. International Journal of Computer Applications (0975 8887) Volume 70 No.18, May 2013

Prediction of Market Capital for Trading Firms through Data Mining Techniques Aditya Nawani Department of Computer Science, Bharati Vidyapeeth s College of Engineering, New Delhi, India Himanshu Gupta

### Machine Learning Algorithms and Predictive Models for Undergraduate Student Retention

, 225 October, 2013, San Francisco, USA Machine Learning Algorithms and Predictive Models for Undergraduate Student Retention Ji-Wu Jia, Member IAENG, Manohar Mareboyana Abstract---In this paper, we have

### An Introduction to Data Mining. Big Data World. Related Fields and Disciplines. What is Data Mining? 2/12/2015

An Introduction to Data Mining for Wind Power Management Spring 2015 Big Data World Every minute: Google receives over 4 million search queries Facebook users share almost 2.5 million pieces of content

### Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification

World of Computer Science and Information Technology Journal (WCSIT) ISSN: 2221-0741 Vol. 2, No. 2, 51-56, 2012 Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification

### EMPIRICAL STUDY ON SELECTION OF TEAM MEMBERS FOR SOFTWARE PROJECTS DATA MINING APPROACH

EMPIRICAL STUDY ON SELECTION OF TEAM MEMBERS FOR SOFTWARE PROJECTS DATA MINING APPROACH SANGITA GUPTA 1, SUMA. V. 2 1 Jain University, Bangalore 2 Dayanada Sagar Institute, Bangalore, India Abstract- One

### International Journal of Computer Science Trends and Technology (IJCST) Volume 2 Issue 3, May-Jun 2014

RESEARCH ARTICLE OPEN ACCESS A Survey of Data Mining: Concepts with Applications and its Future Scope Dr. Zubair Khan 1, Ashish Kumar 2, Sunny Kumar 3 M.Tech Research Scholar 2. Department of Computer

### Classification algorithm in Data mining: An Overview

Classification algorithm in Data mining: An Overview S.Neelamegam #1, Dr.E.Ramaraj *2 #1 M.phil Scholar, Department of Computer Science and Engineering, Alagappa University, Karaikudi. *2 Professor, Department

### PREDICTING STUDENTS PERFORMANCE USING ID3 AND C4.5 CLASSIFICATION ALGORITHMS

PREDICTING STUDENTS PERFORMANCE USING ID3 AND C4.5 CLASSIFICATION ALGORITHMS Kalpesh Adhatrao, Aditya Gaykar, Amiraj Dhawan, Rohit Jha and Vipul Honrao ABSTRACT Department of Computer Engineering, Fr.

### Artificial Neural Network, Decision Tree and Statistical Techniques Applied for Designing and Developing E-mail Classifier

International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-1, Issue-6, January 2013 Artificial Neural Network, Decision Tree and Statistical Techniques Applied for Designing

### Predictive time series analysis of stock prices using neural network classifier

Predictive time series analysis of stock prices using neural network classifier Abhinav Pathak, National Institute of Technology, Karnataka, Surathkal, India abhi.pat93@gmail.com Abstract The work pertains

### FRAUD DETECTION IN ELECTRIC POWER DISTRIBUTION NETWORKS USING AN ANN-BASED KNOWLEDGE-DISCOVERY PROCESS

FRAUD DETECTION IN ELECTRIC POWER DISTRIBUTION NETWORKS USING AN ANN-BASED KNOWLEDGE-DISCOVERY PROCESS Breno C. Costa, Bruno. L. A. Alberto, André M. Portela, W. Maduro, Esdras O. Eler PDITec, Belo Horizonte,

### Data Mining Solutions for the Business Environment

Database Systems Journal vol. IV, no. 4/2013 21 Data Mining Solutions for the Business Environment Ruxandra PETRE University of Economic Studies, Bucharest, Romania ruxandra_stefania.petre@yahoo.com Over

### Neural Networks in Data Mining

IOSR Journal of Engineering (IOSRJEN) ISSN (e): 2250-3021, ISSN (p): 2278-8719 Vol. 04, Issue 03 (March. 2014), V6 PP 01-06 www.iosrjen.org Neural Networks in Data Mining Ripundeep Singh Gill, Ashima Department

### Data Mining Part 5. Prediction

Data Mining Part 5. Prediction 5.1 Spring 2010 Instructor: Dr. Masoud Yaghini Outline Classification vs. Numeric Prediction Prediction Process Data Preparation Comparing Prediction Methods References Classification

### A comparative study of data mining (DM) and massive data mining (MDM)

A comparative study of data mining (DM) and massive data mining (MDM) Prof. Dr. P K Srimani Former Chairman, Dept. of Computer Science and Maths, Bangalore University, Director, R & D, B.U., Bangalore,

### DATA MINING APPROACH FOR PREDICTING STUDENT PERFORMANCE

. Economic Review Journal of Economics and Business, Vol. X, Issue 1, May 2012 /// DATA MINING APPROACH FOR PREDICTING STUDENT PERFORMANCE Edin Osmanbegović *, Mirza Suljić ** ABSTRACT Although data mining

### EFFICIENT DATA PRE-PROCESSING FOR DATA MINING

EFFICIENT DATA PRE-PROCESSING FOR DATA MINING USING NEURAL NETWORKS JothiKumar.R 1, Sivabalan.R.V 2 1 Research scholar, Noorul Islam University, Nagercoil, India Assistant Professor, Adhiparasakthi College

### International Journal of Computer Sciences and Engineering Open Access

International Journal of Computer Sciences and Engineering Open Access Research Paper Volume-4, Issue-2 E-ISSN: 2347-2693 Enhancing Web Learning System Using Multimedia and Regression Algorithm J. Mary

### Web Document Clustering

Web Document Clustering Lab Project based on the MDL clustering suite http://www.cs.ccsu.edu/~markov/mdlclustering/ Zdravko Markov Computer Science Department Central Connecticut State University New Britain,

### Predicting Student Academic Performance at Degree Level: A Case Study

I.J. Intelligent Systems and Applications, 2015, 01, 49-61 Published Online December 2014 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijisa.2015.01.05 Predicting Student Academic Performance at Degree

### An Introduction to Data Mining

An Introduction to Intel Beijing wei.heng@intel.com January 17, 2014 Outline 1 DW Overview What is Notable Application of Conference, Software and Applications Major Process in 2 Major Tasks in Detail

### Using Machine Learning on Sensor Data

Journal of Computing and Information Technology - CIT 18, 2010, 4, 341 347 doi:10.2498/cit.1001913 341 Using Machine Learning on Sensor Data Alexandra Moraru 1,MarkoPesko 1,2, Maria Porcius 3, Carolina

### Index Contents Page No. Introduction . Data Mining & Knowledge Discovery

Index Contents Page No. 1. Introduction 1 1.1 Related Research 2 1.2 Objective of Research Work 3 1.3 Why Data Mining is Important 3 1.4 Research Methodology 4 1.5 Research Hypothesis 4 1.6 Scope 5 2.

### Building A Smart Academic Advising System Using Association Rule Mining

Building A Smart Academic Advising System Using Association Rule Mining Raed Shatnawi +962795285056 raedamin@just.edu.jo Qutaibah Althebyan +962796536277 qaalthebyan@just.edu.jo Baraq Ghalib & Mohammed

### CS 6220: Data Mining Techniques Course Project Description

CS 6220: Data Mining Techniques Course Project Description College of Computer and Information Science Northeastern University Spring 2013 General Goal In this project, you will have an opportunity to

### Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News

Analysis of WEKA Data Mining Algorithm REPTree, Simple Cart and RandomTree for Classification of Indian News Sushilkumar Kalmegh Associate Professor, Department of Computer Science, Sant Gadge Baba Amravati

### DATA MINING TECHNIQUES AND APPLICATIONS

DATA MINING TECHNIQUES AND APPLICATIONS Mrs. Bharati M. Ramageri, Lecturer Modern Institute of Information Technology and Research, Department of Computer Application, Yamunanagar, Nigdi Pune, Maharashtra,

### A STUDY ON DATA MINING INVESTIGATING ITS METHODS, APPROACHES AND APPLICATIONS

A STUDY ON DATA MINING INVESTIGATING ITS METHODS, APPROACHES AND APPLICATIONS Mrs. Jyoti Nawade 1, Dr. Balaji D 2, Mr. Pravin Nawade 3 1 Lecturer, JSPM S Bhivrabai Sawant Polytechnic, Pune (India) 2 Assistant

### A Secured Approach to Credit Card Fraud Detection Using Hidden Markov Model

A Secured Approach to Credit Card Fraud Detection Using Hidden Markov Model Twinkle Patel, Ms. Ompriya Kale Abstract: - As the usage of credit card has increased the credit card fraud has also increased

### ANALYSIS OF INDIAN WEATHER DATA SETS USING DATA MINING TECHNIQUES

ANALYSIS OF INDIAN WEATHER DATA SETS USING DATA MINING TECHNIQUES ABSTRACT T V Rajini kanth 1, V V SSS Balaram 2 and N.Rajasekhar 3 1 Professor, CSE, SNIST, Hyderabad rajinitv@gmail.com 2 Professor & HOD,

### Comparison of Data Mining Techniques used for Financial Data Analysis

Comparison of Data Mining Techniques used for Financial Data Analysis Abhijit A. Sawant 1, P. M. Chawan 2 1 Student, 2 Associate Professor, Department of Computer Technology, VJTI, Mumbai, INDIA Abstract

### Data Mining Application in Enrollment Management: A Case Study

Data Mining Application in Enrollment Management: A Case Study Surjeet Kumar Yadav Research scholar, Shri Venkateshwara University, J. P. Nagar, (U.P.) India ABSTRACT In the last two decades, number of

### Football Match Winner Prediction

Football Match Winner Prediction Kushal Gevaria 1, Harshal Sanghavi 2, Saurabh Vaidya 3, Prof. Khushali Deulkar 4 Department of Computer Engineering, Dwarkadas J. Sanghvi College of Engineering, Mumbai,

### Linear Regression Model for Edu-mining in TES

Linear Regression Model for Edu-mining in TES Prof.Dr.P.K.Srimani Former Director, R &D Division BU, DSI, Bangalore Karnataka, India profsrimanipk@gmail.com Mrs. Malini M Patil Assistant Professor, Dept.

### 8. Machine Learning Applied Artificial Intelligence

8. Machine Learning Applied Artificial Intelligence Prof. Dr. Bernhard Humm Faculty of Computer Science Hochschule Darmstadt University of Applied Sciences 1 Retrospective Natural Language Processing Name

### Comparative Analysis of EM Clustering Algorithm and Density Based Clustering Algorithm Using WEKA tool.

International Journal of Engineering Research and Development e-issn: 2278-067X, p-issn: 2278-800X, www.ijerd.com Volume 9, Issue 8 (January 2014), PP. 19-24 Comparative Analysis of EM Clustering Algorithm

### Predicting the Risk of Heart Attacks using Neural Network and Decision Tree

Predicting the Risk of Heart Attacks using Neural Network and Decision Tree S.Florence 1, N.G.Bhuvaneswari Amma 2, G.Annapoorani 3, K.Malathi 4 PG Scholar, Indian Institute of Information Technology, Srirangam,

### DATA MINING AND REPORTING IN HEALTHCARE

DATA MINING AND REPORTING IN HEALTHCARE Divya Gandhi 1, Pooja Asher 2, Harshada Chaudhari 3 1,2,3 Department of Information Technology, Sardar Patel Institute of Technology, Mumbai,(India) ABSTRACT The

### Keywords Data mining, Classification Algorithm, Decision tree, J48, Random forest, Random tree, LMT, WEKA 3.7. Fig.1. Data mining techniques.

International Journal of Emerging Research in Management &Technology Research Article October 2015 Comparative Study of Various Decision Tree Classification Algorithm Using WEKA Purva Sewaiwar, Kamal Kant

### Evaluation of Feature Selection Methods for Predictive Modeling Using Neural Networks in Credits Scoring

714 Evaluation of Feature election Methods for Predictive Modeling Using Neural Networks in Credits coring Raghavendra B. K. Dr. M.G.R. Educational and Research Institute, Chennai-95 Email: raghavendra_bk@rediffmail.com

### A NEW DECISION TREE METHOD FOR DATA MINING IN MEDICINE

A NEW DECISION TREE METHOD FOR DATA MINING IN MEDICINE Kasra Madadipouya 1 1 Department of Computing and Science, Asia Pacific University of Technology & Innovation ABSTRACT Today, enormous amount of data

### Social Media Mining. Data Mining Essentials

Introduction Data production rate has been increased dramatically (Big Data) and we are able store much more data than before E.g., purchase data, social media data, mobile phone data Businesses and customers

### Predictive Dynamix Inc

Predictive Modeling Technology Predictive modeling is concerned with analyzing patterns and trends in historical and operational data in order to transform data into actionable decisions. This is accomplished

### AUTO CLAIM FRAUD DETECTION USING MULTI CLASSIFIER SYSTEM

AUTO CLAIM FRAUD DETECTION USING MULTI CLASSIFIER SYSTEM ABSTRACT Luis Alexandre Rodrigues and Nizam Omar Department of Electrical Engineering, Mackenzie Presbiterian University, Brazil, São Paulo 71251911@mackenzie.br,nizam.omar@mackenzie.br

### A Perspective Analysis of Traffic Accident using Data Mining Techniques

A Perspective Analysis of Traffic Accident using Data Mining Techniques S.Krishnaveni Ph.D (CS) Research Scholar, Karpagam University, Coimbatore, India 641 021 Dr.M.Hemalatha Asst. Professor & Head, Dept

### Open Access Research on Application of Neural Network in Computer Network Security Evaluation. Shujuan Jin *

Send Orders for Reprints to reprints@benthamscience.ae 766 The Open Electrical & Electronic Engineering Journal, 2014, 8, 766-771 Open Access Research on Application of Neural Network in Computer Network

### ENSEMBLE DECISION TREE CLASSIFIER FOR BREAST CANCER DATA

ENSEMBLE DECISION TREE CLASSIFIER FOR BREAST CANCER DATA D.Lavanya 1 and Dr.K.Usha Rani 2 1 Research Scholar, Department of Computer Science, Sree Padmavathi Mahila Visvavidyalayam, Tirupati, Andhra Pradesh,

96 Business Intelligence Journal January PREDICTION OF CHURN BEHAVIOR OF BANK CUSTOMERS USING DATA MINING TOOLS Dr. U. Devi Prasad Associate Professor Hyderabad Business School GITAM University, Hyderabad

### Data Mining: A Preprocessing Engine

Journal of Computer Science 2 (9): 735-739, 2006 ISSN 1549-3636 2005 Science Publications Data Mining: A Preprocessing Engine Luai Al Shalabi, Zyad Shaaban and Basel Kasasbeh Applied Science University,

### STATISTICA. Financial Institutions. Case Study: Credit Scoring. and

Financial Institutions and STATISTICA Case Study: Credit Scoring STATISTICA Solutions for Business Intelligence, Data Mining, Quality Control, and Web-based Analytics Table of Contents INTRODUCTION: WHAT

### 131-1. Adding New Level in KDD to Make the Web Usage Mining More Efficient. Abstract. 1. Introduction [1]. 1/10

1/10 131-1 Adding New Level in KDD to Make the Web Usage Mining More Efficient Mohammad Ala a AL_Hamami PHD Student, Lecturer m_ah_1@yahoocom Soukaena Hassan Hashem PHD Student, Lecturer soukaena_hassan@yahoocom

### Introducing diversity among the models of multi-label classification ensemble

Introducing diversity among the models of multi-label classification ensemble Lena Chekina, Lior Rokach and Bracha Shapira Ben-Gurion University of the Negev Dept. of Information Systems Engineering and

### International Journal of Computer Science Trends and Technology (IJCST) Volume 3 Issue 3, May-June 2015

RESEARCH ARTICLE OPEN ACCESS Data Mining Technology for Efficient Network Security Management Ankit Naik [1], S.W. Ahmad [2] Student [1], Assistant Professor [2] Department of Computer Science and Engineering

### Data mining techniques: decision trees

Data mining techniques: decision trees 1/39 Agenda Rule systems Building rule systems vs rule systems Quick reference 2/39 1 Agenda Rule systems Building rule systems vs rule systems Quick reference 3/39

### CI6227: Data Mining. Lesson 11b: Ensemble Learning. Data Analytics Department, Institute for Infocomm Research, A*STAR, Singapore.

CI6227: Data Mining Lesson 11b: Ensemble Learning Sinno Jialin PAN Data Analytics Department, Institute for Infocomm Research, A*STAR, Singapore Acknowledgements: slides are adapted from the lecture notes

### ISSN: 2321-7782 (Online) Volume 3, Issue 4, April 2015 International Journal of Advance Research in Computer Science and Management Studies

ISSN: 2321-7782 (Online) Volume 3, Issue 4, April 2015 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online

### King Saud University

King Saud University College of Computer and Information Sciences Department of Computer Science CSC 493 Selected Topics in Computer Science (3-0-1) - Elective Course CECS 493 Selected Topics: DATA MINING

### A Regression Approach for Forecasting Vendor Revenue in Telecommunication Industries

A Regression Approach for Forecasting Vendor Revenue in Telecommunication Industries Aida Mustapha *1, Farhana M. Fadzil #2 * Faculty of Computer Science and Information Technology, Universiti Tun Hussein

### Lecture 10: Regression Trees

Lecture 10: Regression Trees 36-350: Data Mining October 11, 2006 Reading: Textbook, sections 5.2 and 10.5. The next three lectures are going to be about a particular kind of nonlinear predictive model,

### FOREX TRADING PREDICTION USING LINEAR REGRESSION LINE, ARTIFICIAL NEURAL NETWORK AND DYNAMIC TIME WARPING ALGORITHMS

FOREX TRADING PREDICTION USING LINEAR REGRESSION LINE, ARTIFICIAL NEURAL NETWORK AND DYNAMIC TIME WARPING ALGORITHMS Leslie C.O. Tiong 1, David C.L. Ngo 2, and Yunli Lee 3 1 Sunway University, Malaysia,

### ANALYSIS OF FEATURE SELECTION WITH CLASSFICATION: BREAST CANCER DATASETS

ANALYSIS OF FEATURE SELECTION WITH CLASSFICATION: BREAST CANCER DATASETS Abstract D.Lavanya * Department of Computer Science, Sri Padmavathi Mahila University Tirupati, Andhra Pradesh, 517501, India lav_dlr@yahoo.com

### Classification of Learners Using Linear Regression

Proceedings of the Federated Conference on Computer Science and Information Systems pp. 717 721 ISBN 978-83-60810-22-4 Classification of Learners Using Linear Regression Marian Cristian Mihăescu Software

### SVM Ensemble Model for Investment Prediction

19 SVM Ensemble Model for Investment Prediction Chandra J, Assistant Professor, Department of Computer Science, Christ University, Bangalore Siji T. Mathew, Research Scholar, Christ University, Dept of

### Customer Classification And Prediction Based On Data Mining Technique

Customer Classification And Prediction Based On Data Mining Technique Ms. Neethu Baby 1, Mrs. Priyanka L.T 2 1 M.E CSE, Sri Shakthi Institute of Engineering and Technology, Coimbatore 2 Assistant Professor

### Research Phases of University Data Mining Project Development

Research Phases of University Data Mining Project Development Dorina Kabakchieva 1, Kamelia Stefanova 2, Valentin Kissimov 3, and Roumen Nikolov 4 1 Sofia University St. Kl. Ohridski, 125 Tzarigradsko

### Data Mining with Weka

Data Mining with Weka Class 1 Lesson 1 Introduction Ian H. Witten Department of Computer Science University of Waikato New Zealand weka.waikato.ac.nz Data Mining with Weka a practical course on how to

### Course Syllabus. Purposes of Course:

Course Syllabus Eco 5385.701 Predictive Analytics for Economists Summer 2014 TTh 6:00 8:50 pm and Sat. 12:00 2:50 pm First Day of Class: Tuesday, June 3 Last Day of Class: Tuesday, July 1 251 Maguire Building

### Automatic Resolver Group Assignment of IT Service Desk Outsourcing

Automatic Resolver Group Assignment of IT Service Desk Outsourcing in Banking Business Padej Phomasakha Na Sakolnakorn*, Phayung Meesad ** and Gareth Clayton*** Abstract This paper proposes a framework

### Data Mining - Evaluation of Classifiers

Data Mining - Evaluation of Classifiers Lecturer: JERZY STEFANOWSKI Institute of Computing Sciences Poznan University of Technology Poznan, Poland Lecture 4 SE Master Course 2008/2009 revised for 2010

### HYBRID PROBABILITY BASED ENSEMBLES FOR BANKRUPTCY PREDICTION

HYBRID PROBABILITY BASED ENSEMBLES FOR BANKRUPTCY PREDICTION Chihli Hung 1, Jing Hong Chen 2, Stefan Wermter 3, 1,2 Department of Management Information Systems, Chung Yuan Christian University, Taiwan

### Clustering on Large Numeric Data Sets Using Hierarchical Approach Birch

Global Journal of Computer Science and Technology Software & Data Engineering Volume 12 Issue 12 Version 1.0 Year 2012 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global

### Data Mining Applications for Smart Grid in Japan

1 Paper ID: 14TD0194 Data Mining Applications for Smart Grid in Japan Hiroyuki Mori Dept. of Network Design Meiji University Nakano-ku, Tokyo 171-0042 Japan hmori@isc.meiji.ac.jp 2 OUTLINE 1. Objective

### TOWARDS SIMPLE, EASY TO UNDERSTAND, AN INTERACTIVE DECISION TREE ALGORITHM

TOWARDS SIMPLE, EASY TO UNDERSTAND, AN INTERACTIVE DECISION TREE ALGORITHM Thanh-Nghi Do College of Information Technology, Cantho University 1 Ly Tu Trong Street, Ninh Kieu District Cantho City, Vietnam

### Comparison of K-means and Backpropagation Data Mining Algorithms

Comparison of K-means and Backpropagation Data Mining Algorithms Nitu Mathuriya, Dr. Ashish Bansal Abstract Data mining has got more and more mature as a field of basic research in computer science and

### Indian Agriculture Land through Decision Tree in Data Mining

Indian Agriculture Land through Decision Tree in Data Mining Kamlesh Kumar Joshi, M.Tech(Pursuing 4 th Sem) Laxmi Narain College of Technology, Indore (M.P) India k3g.kamlesh@gmail.com 9926523514 Pawan

### Data Mining in Education: Data Classification and Decision Tree Approach

Data Mining in Education: Data Classification and Decision Tree Approach Sonali Agarwal, G. N. Pandey, and M. D. Tiwari Abstract Educational organizations are one of the important parts of our society

### Classification Algorithms in Intrusion Detection System: A Survey

Classification Algorithms in Intrusion Detection System: A Survey V. Jaiganesh 1 Dr. P. Sumathi 2 A.Vinitha 3 1 Doctoral Research Scholar, Department of Computer Science, Manonmaniam Sundaranar University,

### DEA implementation and clustering analysis using the K-Means algorithm

Data Mining VI 321 DEA implementation and clustering analysis using the K-Means algorithm C. A. A. Lemos, M. P. E. Lins & N. F. F. Ebecken COPPE/Universidade Federal do Rio de Janeiro, Brazil Abstract

### Université de Montpellier 2 Hugo Alatrista-Salas : hugo.alatrista-salas@teledetection.fr

Université de Montpellier 2 Hugo Alatrista-Salas : hugo.alatrista-salas@teledetection.fr WEKA Gallirallus Zeland) australis : Endemic bird (New Characteristics Waikato university Weka is a collection

### SPATIAL DATA CLASSIFICATION AND DATA MINING

, pp.-40-44. Available online at http://www. bioinfo. in/contents. php?id=42 SPATIAL DATA CLASSIFICATION AND DATA MINING RATHI J.B. * AND PATIL A.D. Department of Computer Science & Engineering, Jawaharlal

### COMP3420: Advanced Databases and Data Mining. Classification and prediction: Introduction and Decision Tree Induction

COMP3420: Advanced Databases and Data Mining Classification and prediction: Introduction and Decision Tree Induction Lecture outline Classification versus prediction Classification A two step process Supervised

### Data Mining Applications in Higher Education

Executive report Data Mining Applications in Higher Education Jing Luan, PhD Chief Planning and Research Officer, Cabrillo College Founder, Knowledge Discovery Laboratories Table of contents Introduction..............................................................2

### Impelling Heart Attack Prediction System using Data Mining and Artificial Neural Network

General Article International Journal of Current Engineering and Technology E-ISSN 2277 4106, P-ISSN 2347-5161 2014 INPRESSCO, All Rights Reserved Available at http://inpressco.com/category/ijcet Impelling

### Data Mining Classification: Decision Trees

Data Mining Classification: Decision Trees Classification Decision Trees: what they are and how they work Hunt s (TDIDT) algorithm How to select the best split How to handle Inconsistent data Continuous

### Learning outcomes. Knowledge and understanding. Competence and skills

Syllabus Master s Programme in Statistics and Data Mining 120 ECTS Credits Aim The rapid growth of databases provides scientists and business people with vast new resources. This programme meets the challenges

### Credit Card Fraud Detection Using Self Organised Map

International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 13 (2014), pp. 1343-1348 International Research Publications House http://www. irphouse.com Credit Card Fraud