# Principles of Dat Da a t Mining Pham Tho Hoan n

Save this PDF as:

Size: px
Start display at page:

Download "Principles of Dat Da a t Mining Pham Tho Hoan hoanpt@hnue.edu.v hoanpt@hnue.edu. n"

## Transcription

1 Principles of Data Mining Pham Tho Hoan

2 References [1] David Hand, Heikki Mannila and Padhraic Smyth, Principles of Data Mining, MIT press, 2002 [2] Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, 2 nd Edition, i [3] Christopher M. Bishop, Pattern Recognition and Machine Learning, 2006

3 Principles of Data Mining

4 What is data mining Data mining is the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner. The relationships and summaries derived through a data mining exercise are often referred to as models or patterns. Examples include linear equations, rules, clusters, graphs, tree structures, and recurrent patterns in time series. Observational data experimental data, convenience (opportunity) samples random samples, huge data small data, data mining statistics Novelty >< triviality, novelty must be measured relative to the user's prior knowledge Simple relationships are more readily understood than complicated ones, and may well be preferred, but simple ones may not be useful.

5 Data mining and Knowledge ld Discovery in Data

6 Types of data sets n p data matrix {real number, category, missing, noise} Text, sequence, structure, pictures Transactions Etc. Lost information

7 Model and pattern structures A model structure, as defined here, is a global summary of a data set; it makes statements about any point in the full measurement space. (Y = ax + c ) Pattern structures make statements only about restricted regions of the space spanned by the variables. An example is a simple probabilistic statement of the form: if X > x1 then prob(y > y1) = p1; or p(y > y1 X > x1) = p1. This structure consists of constraints on the values of the variables X and Y, related din the form of a probabilistic bili i rule Once we have established the structural form we are interested in finding, the next step is to estimate its parameters from the available data. We refer to a particular model, dlsuch as y = 32 3:2x :8, as a "fitted d model," dl" or just "model" for short (and similarly for patterns).

8 Data mining tasks Exploratory Data Analysis (EDA) the goal is simply to explore the data without any clear ideas of what we are looking for. Typically, EDA techniques are interactive and visual, and there are many effective graphical display methods for relatively small, low dimensional data sets. Descriptive Modeling The goal of a descriptive model is describe all of the data (or the process generating the data). Examples of such descriptions include models for the overall probability distribution of the data (density estimation), partitioning of the p dimensional space into groups (cluster analysis and segmentation), and models describing the relationship between variables (dependency modeling). Predictive Modeling: Classification and Regression The aim here is to build a model that will permit the value of one variable to be predicted from the known values of other variables. Discovering Patterns and Rules Retrieval by Content

9 Components of data mining algorithms 1. Model or Pattern Structure: determining the underlying structure or functional forms that we seek from the data 2. Score Function: judging the quality of a fitted model 3. Optimization and Search Method: optimizing the score function and searching over different model dl and pattern structures. 4. Data Management Strategy: handling data access efficiently during the search/optimization

10 Score functions Without some form of score function, we cannot tell whether one model is better than another or, indeed, how to choose a good set of values for the parameters of the model. Several score functions are widely used for this purpose; these include likelihood, sum of squared errors, and misclassification rate (the latter is used in supervised classification problems). Penalize model complexity: score(model) = error(model) + penaltyfunction(model), l)

11 Optimization and Search Methods The goal of optimizationandsearchisand is to determine the structure and the parameter values that achieve a minimum (or maximum, depending on the context) value of the score function. Methods: Greedy Search Algorithm, Systematic Search and Search Heuristics, Branch and Bound, Gradient Based Methods for Optimizing Smooth Functions, Univariate Parameter Optimization, Multivariate Parameter Optimization, Constrained Optimization, etc.

12 Data Management Strategy The ways in which the data are stored The ways in which the data are stored, indexed, and accessed.

13 An example Problem: Input: a dataset of credit card spending {(x i, y i ), i=1,.., n}; Output: a model which would allow us to predict a person's annual credit card spending ggiven their annual income. One solution: the model would not be perfect, but since spending typically increases with income, the model might well be adequate as a rough characterization. Model structure: variable spending (f) is linearly related to the variable income (x): f(x) = ax +b [ yi f ( xi )] The score function: 2 The smaller this sum is, the better the model fits the data. The optimization algorithm (to find a, b) is quite simple in the case of linear regression: a and b can be expressed as explicit i functions of the observed values of spending and income.

14 Some questions Cùng bài toán trên (mô hình hóa quan hệ giữa x và y), xem xét 3 mô hình sau, anh chị thích mô hình nào? M1: y=(y y n )/n vớimọix M2: y=ax + b (với a, b tìm được như trong slide trước) M3: if (x=x 1 ) then y=y 1 else if (x=x 2 ) then y=y 2 else if (x=x n ) then y=y n else y=random-value (default) Mô hình nào phức tạpnhất? Mô hình nào phù hợp vớidữ liệuhuấnluyện nhất? Mô hình nào có khả năng dự đoán tốtnhất?

### Principles of Data Mining by Hand&Mannila&Smyth

Principles of Data Mining by Hand&Mannila&Smyth Slides for Textbook Ari Visa,, Institute of Signal Processing Tampere University of Technology October 4, 2010 Data Mining: Concepts and Techniques 1 Differences

### Data Mining and Exploration. Data Mining and Exploration: Introduction. Relationships between courses. Overview. Course Introduction

Data Mining and Exploration Data Mining and Exploration: Introduction Amos Storkey, School of Informatics January 10, 2006 http://www.inf.ed.ac.uk/teaching/courses/dme/ Course Introduction Welcome Administration

### CS 2750 Machine Learning. Lecture 1. Machine Learning. http://www.cs.pitt.edu/~milos/courses/cs2750/ CS 2750 Machine Learning.

Lecture Machine Learning Milos Hauskrecht milos@cs.pitt.edu 539 Sennott Square, x5 http://www.cs.pitt.edu/~milos/courses/cs75/ Administration Instructor: Milos Hauskrecht milos@cs.pitt.edu 539 Sennott

### Principles of Data Mining

Principles of Data Mining Instructor: Sargur N. 1 University at Buffalo The State University of New York srihari@cedar.buffalo.edu Introduction: Topics 1. Introduction to Data Mining 2. Nature of Data

### CS 2750 Machine Learning. Lecture 1. Machine Learning. CS 2750 Machine Learning.

Lecture 1 Machine Learning Milos Hauskrecht milos@cs.pitt.edu 539 Sennott Square, x-5 http://www.cs.pitt.edu/~milos/courses/cs75/ Administration Instructor: Milos Hauskrecht milos@cs.pitt.edu 539 Sennott

### Why do statisticians "hate" us?

Why do statisticians "hate" us? David Hand, Heikki Mannila, Padhraic Smyth "Data mining is the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data

### Dynamic Data in terms of Data Mining Streams

International Journal of Computer Science and Software Engineering Volume 2, Number 1 (2015), pp. 1-6 International Research Publication House http://www.irphouse.com Dynamic Data in terms of Data Mining

### 203.4770: Introduction to Machine Learning Dr. Rita Osadchy

203.4770: Introduction to Machine Learning Dr. Rita Osadchy 1 Outline 1. About the Course 2. What is Machine Learning? 3. Types of problems and Situations 4. ML Example 2 About the course Course Homepage:

### Statistical Models in Data Mining

Statistical Models in Data Mining Sargur N. Srihari University at Buffalo The State University of New York Department of Computer Science and Engineering Department of Biostatistics 1 Srihari Flood of

### Machine Learning and Statistics: What s the Connection?

Machine Learning and Statistics: What s the Connection? Institute for Adaptive and Neural Computation School of Informatics, University of Edinburgh, UK August 2006 Outline The roots of machine learning

### Knowledge Discovery from Data Bases Proposal for a MAP-I UC

Knowledge Discovery from Data Bases Proposal for a MAP-I UC João Gama (jgama@fep.up.pt) Universidade do Porto 1 Knowledge Discovery from Data Bases We are deluged by data: scientific data, medical data,

### Machine Learning and Data Mining. Regression Problem. (adapted from) Prof. Alexander Ihler

Machine Learning and Data Mining Regression Problem (adapted from) Prof. Alexander Ihler Overview Regression Problem Definition and define parameters ϴ. Prediction using ϴ as parameters Measure the error

### Introduction to Learning & Decision Trees

Artificial Intelligence: Representation and Problem Solving 5-38 April 0, 2007 Introduction to Learning & Decision Trees Learning and Decision Trees to learning What is learning? - more than just memorizing

### CAS CS 565, Data Mining

CAS CS 565, Data Mining Course logistics Course webpage: http://www.cs.bu.edu/~evimaria/cs565-10.html Schedule: Mon Wed, 4-5:30 Instructor: Evimaria Terzi, evimaria@cs.bu.edu Office hours: Mon 2:30-4pm,

### A Comparative Study of clustering algorithms Using weka tools

A Comparative Study of clustering algorithms Using weka tools Bharat Chaudhari 1, Manan Parikh 2 1,2 MECSE, KITRC KALOL ABSTRACT Data clustering is a process of putting similar data into groups. A clustering

### Search and Data Mining: Techniques. Applications Anya Yarygina Boris Novikov

Search and Data Mining: Techniques Applications Anya Yarygina Boris Novikov Introduction Data mining applications Data mining system products and research prototypes Additional themes on data mining Social

### Introduction to Machine Learning

Introduction to Machine Learning Prof. Alexander Ihler Prof. Max Welling icamp Tutorial July 22 What is machine learning? The ability of a machine to improve its performance based on previous results:

### Data Mining Analytics for Business Intelligence and Decision Support

Data Mining Analytics for Business Intelligence and Decision Support Chid Apte, T.J. Watson Research Center, IBM Research Division Knowledge Discovery and Data Mining (KDD) techniques are used for analyzing

### Three Perspectives of Data Mining

Three Perspectives of Data Mining Zhi-Hua Zhou * National Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China Abstract This paper reviews three recent books on data mining

### 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,

### CHAPTER 3 DATA MINING AND CLUSTERING

CHAPTER 3 DATA MINING AND CLUSTERING 3.1 Introduction Nowadays, large quantities of data are being accumulated. The amount of data collected is said to be almost doubled every 9 months. Seeking knowledge

### Support Vector Machines with Clustering for Training with Very Large Datasets

Support Vector Machines with Clustering for Training with Very Large Datasets Theodoros Evgeniou Technology Management INSEAD Bd de Constance, Fontainebleau 77300, France theodoros.evgeniou@insead.fr Massimiliano

### Title. Introduction to Data Mining. Dr Arulsivanathan Naidoo Statistics South Africa. OECD Conference Cape Town 8-10 December 2010.

Title Introduction to Data Mining Dr Arulsivanathan Naidoo Statistics South Africa OECD Conference Cape Town 8-10 December 2010 1 Outline Introduction Statistics vs Knowledge Discovery Predictive Modeling

### Classification and Prediction

Classification and Prediction Slides for Data Mining: Concepts and Techniques Chapter 7 Jiawei Han and Micheline Kamber Intelligent Database Systems Research Lab School of Computing Science Simon Fraser

### Data Mining and Neural Networks in Stata

Data Mining and Neural Networks in Stata 2 nd Italian Stata Users Group Meeting Milano, 10 October 2005 Mario Lucchini e Maurizo Pisati Università di Milano-Bicocca mario.lucchini@unimib.it maurizio.pisati@unimib.it

### Foundations of Artificial Intelligence. Introduction to Data Mining

Foundations of Artificial Intelligence Introduction to Data Mining Objectives Data Mining Introduce a range of data mining techniques used in AI systems including : Neural networks Decision trees Present

### Lecture 20: Clustering

Lecture 20: Clustering Wrap-up of neural nets (from last lecture Introduction to unsupervised learning K-means clustering COMP-424, Lecture 20 - April 3, 2013 1 Unsupervised learning In supervised learning,

### Assessing Data Mining: The State of the Practice

Assessing Data Mining: The State of the Practice 2003 Herbert A. Edelstein Two Crows Corporation 10500 Falls Road Potomac, Maryland 20854 www.twocrows.com (301) 983-3555 Objectives Separate myth from reality

### C19 Machine Learning

C9 Machine Learning 8 Lectures Hilary Term 25 2 Tutorial Sheets A. Zisserman Overview: Supervised classification perceptron, support vector machine, loss functions, kernels, random forests, neural networks

### 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

### Introduction to Artificial Intelligence G51IAI. An Introduction to Data Mining

Introduction to Artificial Intelligence G51IAI An Introduction to Data Mining Learning Objectives Introduce a range of data mining techniques used in AI systems including : Neural networks Decision trees

### Machine Learning. CUNY Graduate Center, Spring 2013. Professor Liang Huang. huang@cs.qc.cuny.edu

Machine Learning CUNY Graduate Center, Spring 2013 Professor Liang Huang huang@cs.qc.cuny.edu http://acl.cs.qc.edu/~lhuang/teaching/machine-learning Logistics Lectures M 9:30-11:30 am Room 4419 Personnel

### Data Mining for Model Creation. Presentation by Paul Below, EDS 2500 NE Plunkett Lane Poulsbo, WA USA 98370 paul.below@eds.

Sept 03-23-05 22 2005 Data Mining for Model Creation Presentation by Paul Below, EDS 2500 NE Plunkett Lane Poulsbo, WA USA 98370 paul.below@eds.com page 1 Agenda Data Mining and Estimating Model Creation

Data Mining: An Overview David Madigan http://www.stat.columbia.edu/~madigan Overview Brief Introduction to Data Mining Data Mining Algorithms Specific Eamples Algorithms: Disease Clusters Algorithms:

### Data Mining Chapter 6: Models and Patterns Fall 2011 Ming Li Department of Computer Science and Technology Nanjing University

Data Mining Chapter 6: Models and Patterns Fall 2011 Ming Li Department of Computer Science and Technology Nanjing University Models vs. Patterns Models A model is a high level, global description of a

### LABEL PROPAGATION ON GRAPHS. SEMI-SUPERVISED LEARNING. ----Changsheng Liu 10-30-2014

LABEL PROPAGATION ON GRAPHS. SEMI-SUPERVISED LEARNING ----Changsheng Liu 10-30-2014 Agenda Semi Supervised Learning Topics in Semi Supervised Learning Label Propagation Local and global consistency Graph

### Statistics 215b 11/20/03 D.R. Brillinger. A field in search of a definition a vague concept

Statistics 215b 11/20/03 D.R. Brillinger Data mining A field in search of a definition a vague concept D. Hand, H. Mannila and P. Smyth (2001). Principles of Data Mining. MIT Press, Cambridge. Some definitions/descriptions

### Static Data Mining Algorithm with Progressive Approach for Mining Knowledge

Global Journal of Business Management and Information Technology. Volume 1, Number 2 (2011), pp. 85-93 Research India Publications http://www.ripublication.com Static Data Mining Algorithm with Progressive

### Data, Measurements, Features

Data, Measurements, Features Middle East Technical University Dep. of Computer Engineering 2009 compiled by V. Atalay What do you think of when someone says Data? We might abstract the idea that data are

### EFFECTIVE USE OF THE KDD PROCESS AND DATA MINING FOR COMPUTER PERFORMANCE PROFESSIONALS

EFFECTIVE USE OF THE KDD PROCESS AND DATA MINING FOR COMPUTER PERFORMANCE PROFESSIONALS Susan P. Imberman Ph.D. College of Staten Island, City University of New York Imberman@postbox.csi.cuny.edu Abstract

### An Overview of Knowledge Discovery Database and Data mining Techniques

An Overview of Knowledge Discovery Database and Data mining Techniques Priyadharsini.C 1, Dr. Antony Selvadoss Thanamani 2 M.Phil, Department of Computer Science, NGM College, Pollachi, Coimbatore, Tamilnadu,

### DATA MINING WITH DIFFERENT TYPES OF X-RAY DATA

315 DATA MINING WITH DIFFERENT TYPES OF X-RAY DATA C. K. Lowe-Ma, A. E. Chen, D. Scholl Physical & Environmental Sciences, Research and Advanced Engineering Ford Motor Company, Dearborn, Michigan, USA

### Sanjeev Kumar. contribute

RESEARCH ISSUES IN DATAA MINING Sanjeev Kumar I.A.S.R.I., Library Avenue, Pusa, New Delhi-110012 sanjeevk@iasri.res.in 1. Introduction The field of data mining and knowledgee discovery is emerging as a

### DATA MINING TECHNOLOGY. Keywords: data mining, data warehouse, knowledge discovery, OLAP, OLAM.

DATA MINING TECHNOLOGY Georgiana Marin 1 Abstract In terms of data processing, classical statistical models are restrictive; it requires hypotheses, the knowledge and experience of specialists, equations,

### 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

### Information Management course

Università degli Studi di Milano Master Degree in Computer Science Information Management course Teacher: Alberto Ceselli Lecture 01 : 06/10/2015 Practical informations: Teacher: Alberto Ceselli (alberto.ceselli@unimi.it)

### Fig. 1 A typical Knowledge Discovery process [2]

Volume 4, Issue 7, July 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering Research Paper Available online at: www.ijarcsse.com A Review on Clustering

### Data Mining and Knowledge Discovery in Databases (KDD) State of the Art. Prof. Dr. T. Nouri Computer Science Department FHNW Switzerland

Data Mining and Knowledge Discovery in Databases (KDD) State of the Art Prof. Dr. T. Nouri Computer Science Department FHNW Switzerland 1 Conference overview 1. Overview of KDD and data mining 2. Data

### Principles of Data Mining by David Hand, Heikki Mannila and Padhraic Smyth The MIT Press 2001 (546 pages)

Principles of Data Mining by David Hand, Heikki Mannila and Padhraic Smyth The MIT Press 2001 (546 pages) ISBN: 026208290x A comprehensive, highly technical look at the math and science behind extracting

### 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

### The KDD Process for Extracting Useful Knowledge from Volumes of Data

Knowledge Discovery in bases creates the context for developing the tools needed to control the flood of data facing organizations that depend on ever-growing databases of business, manufacturing, scientific,

### { Mining, Sets, of, Patterns }

{ Mining, Sets, of, Patterns } A tutorial at ECMLPKDD2010 September 20, 2010, Barcelona, Spain by B. Bringmann, S. Nijssen, N. Tatti, J. Vreeken, A. Zimmermann 1 Overview Tutorial 00:00 00:45 Introduction

### Data Mining. for Process Improvement DATA MINING. Paul Below, Quantitative Software Management, Inc. (QSM)

Data mining techniques can be used to help thin out the forest so that we can examine the important trees. Hopefully, this article will encourage you to learn more about data mining, try some of the techniques

### Introduction to Data Science: CptS 483-06 Syllabus First Offering: Fall 2015

Course Information Introduction to Data Science: CptS 483-06 Syllabus First Offering: Fall 2015 Credit Hours: 3 Semester: Fall 2015 Meeting times and location: MWF, 12:10 13:00, Sloan 163 Course website:

### 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

### Machine Learning and Pattern Recognition Logistic Regression

Machine Learning and Pattern Recognition Logistic Regression Course Lecturer:Amos J Storkey Institute for Adaptive and Neural Computation School of Informatics University of Edinburgh Crichton Street,

### Unsupervised Data Mining (Clustering)

Unsupervised Data Mining (Clustering) Javier Béjar KEMLG December 01 Javier Béjar (KEMLG) Unsupervised Data Mining (Clustering) December 01 1 / 51 Introduction Clustering in KDD One of the main tasks in

### Block Diagram Reduction

Appendix W Block Diagram Reduction W.3 4Mason s Rule and the Signal-Flow Graph A compact alternative notation to the block diagram is given by the signal- ow graph introduced Signal- ow by S. J. Mason

### A Logistic Regression Approach to Ad Click Prediction

A Logistic Regression Approach to Ad Click Prediction Gouthami Kondakindi kondakin@usc.edu Satakshi Rana satakshr@usc.edu Aswin Rajkumar aswinraj@usc.edu Sai Kaushik Ponnekanti ponnekan@usc.edu Vinit Parakh

### City University of Hong Kong. Information on a Course offered by Department of Computer Science with effect from Semester A in 2014 / 2015

City University of Hong Kong Information on a Course offered by Department of Computer Science with effect from Semester A in 2014 / 2015 Part I Course Title: Fundamentals of Data Science Course Code:

### Perspectives on Data Mining

Perspectives on Data Mining Niall Adams Department of Mathematics, Imperial College London n.adams@imperial.ac.uk April 2009 Objectives Give an introductory overview of data mining (DM) (or Knowledge Discovery

### 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

### A Statistical Text Mining Method for Patent Analysis

A Statistical Text Mining Method for Patent Analysis Department of Statistics Cheongju University, shjun@cju.ac.kr Abstract Most text data from diverse document databases are unsuitable for analytical

### Knowledge Discovery from Data Bases Proposal for a MAP-I UC

Knowledge Discovery from Data Bases Proposal for a MAP-I UC P. Brazdil 1, João Gama 1, P. Azevedo 2 1 Universidade do Porto; 2 Universidade do Minho; 1 Knowledge Discovery from Data Bases We are deluged

### Introduction to Data Mining. Lijun Zhang zlj@nju.edu.cn http://cs.nju.edu.cn/zlj

Introduction to Data Mining Lijun Zhang zlj@nju.edu.cn http://cs.nju.edu.cn/zlj Outline Overview Introduction The Data Mining Process The Basic Data Types The Major Building Blocks Scalability and Streaming

### Graph Mining and Social Network Analysis

Graph Mining and Social Network Analysis Data Mining and Text Mining (UIC 583 @ Politecnico di Milano) References Jiawei Han and Micheline Kamber, "Data Mining: Concepts and Techniques", The Morgan Kaufmann

### Silvermine House Steenberg Office Park, Tokai 7945 Cape Town, South Africa Telephone: +27 21 702 4666 www.spss-sa.com

SPSS-SA Silvermine House Steenberg Office Park, Tokai 7945 Cape Town, South Africa Telephone: +27 21 702 4666 www.spss-sa.com SPSS-SA Training Brochure 2009 TABLE OF CONTENTS 1 SPSS TRAINING COURSES FOCUSING

### 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,

### Statistical Machine Learning

Statistical Machine Learning UoC Stats 37700, Winter quarter Lecture 4: classical linear and quadratic discriminants. 1 / 25 Linear separation For two classes in R d : simple idea: separate the classes

### ANALYTICAL TECHNIQUES FOR DATA VISUALIZATION

ANALYTICAL TECHNIQUES FOR DATA VISUALIZATION CSE 537 Ar@ficial Intelligence Professor Anita Wasilewska GROUP 2 TEAM MEMBERS: SAEED BOOR BOOR - 110564337 SHIH- YU TSAI - 110385129 HAN LI 110168054 SOURCES

### Classification of Bad Accounts in Credit Card Industry

Classification of Bad Accounts in Credit Card Industry Chengwei Yuan December 12, 2014 Introduction Risk management is critical for a credit card company to survive in such competing industry. In addition

### Data Mining Project Report. Document Clustering. Meryem Uzun-Per

Data Mining Project Report Document Clustering Meryem Uzun-Per 504112506 Table of Content Table of Content... 2 1. Project Definition... 3 2. Literature Survey... 3 3. Methods... 4 3.1. K-means algorithm...

### Data Mining for Digital Forensics

Digital Forensics - CS489 Sep 15, 2006 Topical Paper Mayuri Shakamuri Data Mining for Digital Forensics Introduction "Data mining is the analysis of (often large) observational data sets to find unsuspected

### A Lightweight Solution to the Educational Data Mining Challenge

A Lightweight Solution to the Educational Data Mining Challenge Kun Liu Yan Xing Faculty of Automation Guangdong University of Technology Guangzhou, 510090, China catch0327@yahoo.com yanxing@gdut.edu.cn

### Data Mining Chapter 1: Introduction Fall 2011 Ming Li Department of Computer Science and Technology Nanjing University

Data Mining Chapter 1: Introduction Fall 2011 Ming Li Department of Computer Science and Technology Nanjing University Advances in computer technology Computer Hardware Super computers and high performance

### STA 4273H: Statistical Machine Learning

STA 4273H: Statistical Machine Learning Russ Salakhutdinov Department of Statistics! rsalakhu@utstat.toronto.edu! http://www.cs.toronto.edu/~rsalakhu/ Lecture 6 Three Approaches to Classification Construct

### 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

### Exploratory Data Analysis with MATLAB

Computer Science and Data Analysis Series Exploratory Data Analysis with MATLAB Second Edition Wendy L Martinez Angel R. Martinez Jeffrey L. Solka ( r ec) CRC Press VV J Taylor & Francis Group Boca Raton

### Distributed forests for MapReduce-based machine learning

Distributed forests for MapReduce-based machine learning Ryoji Wakayama, Ryuei Murata, Akisato Kimura, Takayoshi Yamashita, Yuji Yamauchi, Hironobu Fujiyoshi Chubu University, Japan. NTT Communication

### Data Mining Cluster Analysis: Basic Concepts and Algorithms. Lecture Notes for Chapter 8. Introduction to Data Mining

Data Mining Cluster Analysis: Basic Concepts and Algorithms Lecture Notes for Chapter 8 by Tan, Steinbach, Kumar 1 What is Cluster Analysis? Finding groups of objects such that the objects in a group will

### Lluis Belanche + Alfredo Vellido. Intelligent Data Analysis and Data Mining

Lluis Belanche + Alfredo Vellido Intelligent Data Analysis and Data Mining a.k.a. Data Mining II Office 319, Omega, BCN EET, office 107, TR 2, Terrassa avellido@lsi.upc.edu skype, gtalk: avellido Tels.:

### Data Preprocessing. Week 2

Data Preprocessing Week 2 Topics Data Types Data Repositories Data Preprocessing Present homework assignment #1 Team Homework Assignment #2 Read pp. 227 240, pp. 250 250, and pp. 259 263 the text book.

### EXPLORING & MODELING USING INTERACTIVE DECISION TREES IN SAS ENTERPRISE MINER. Copyr i g ht 2013, SAS Ins titut e Inc. All rights res er ve d.

EXPLORING & MODELING USING INTERACTIVE DECISION TREES IN SAS ENTERPRISE MINER ANALYTICS LIFECYCLE Evaluate & Monitor Model Formulate Problem Data Preparation Deploy Model Data Exploration Validate Models

### NAVIGATING SCIENTIFIC LITERATURE A HOLISTIC PERSPECTIVE. Venu Govindaraju

NAVIGATING SCIENTIFIC LITERATURE A HOLISTIC PERSPECTIVE Venu Govindaraju BIOMETRICS DOCUMENT ANALYSIS PATTERN RECOGNITION 8/24/2015 ICDAR- 2015 2 Towards a Globally Optimal Approach for Learning Deep Unsupervised

### Introduction to machine learning and pattern recognition Lecture 1 Coryn Bailer-Jones

Introduction to machine learning and pattern recognition Lecture 1 Coryn Bailer-Jones http://www.mpia.de/homes/calj/mlpr_mpia2008.html 1 1 What is machine learning? Data description and interpretation

### Big Data - Lecture 1 Optimization reminders

Big Data - Lecture 1 Optimization reminders S. Gadat Toulouse, Octobre 2014 Big Data - Lecture 1 Optimization reminders S. Gadat Toulouse, Octobre 2014 Schedule Introduction Major issues Examples Mathematics

### Lecture 2: Descriptive Statistics and Exploratory Data Analysis

Lecture 2: Descriptive Statistics and Exploratory Data Analysis Further Thoughts on Experimental Design 16 Individuals (8 each from two populations) with replicates Pop 1 Pop 2 Randomly sample 4 individuals

### Predict the Popularity of YouTube Videos Using Early View Data

000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 015 016 017 018 019 020 021 022 023 024 025 026 027 028 029 030 031 032 033 034 035 036 037 038 039 040 041 042 043 044 045 046 047 048 049 050

### Nine Common Types of Data Mining Techniques Used in Predictive Analytics

1 Nine Common Types of Data Mining Techniques Used in Predictive Analytics By Laura Patterson, President, VisionEdge Marketing Predictive analytics enable you to develop mathematical models to help better

### An Enhanced Clustering Algorithm to Analyze Spatial Data

International Journal of Engineering and Technical Research (IJETR) ISSN: 2321-0869, Volume-2, Issue-7, July 2014 An Enhanced Clustering Algorithm to Analyze Spatial Data Dr. Mahesh Kumar, Mr. Sachin Yadav

### PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION

PATTERN RECOGNITION AND MACHINE LEARNING CHAPTER 4: LINEAR MODELS FOR CLASSIFICATION Introduction In the previous chapter, we explored a class of regression models having particularly simple analytical

### College information system research based on data mining

2009 International Conference on Machine Learning and Computing IPCSIT vol.3 (2011) (2011) IACSIT Press, Singapore College information system research based on data mining An-yi Lan 1, Jie Li 2 1 Hebei

### Clustering. 15-381 Artificial Intelligence Henry Lin. Organizing data into clusters such that there is

Clustering 15-381 Artificial Intelligence Henry Lin Modified from excellent slides of Eamonn Keogh, Ziv Bar-Joseph, and Andrew Moore What is Clustering? Organizing data into clusters such that there is

### HIGH DIMENSIONAL UNSUPERVISED CLUSTERING BASED FEATURE SELECTION ALGORITHM

HIGH DIMENSIONAL UNSUPERVISED CLUSTERING BASED FEATURE SELECTION ALGORITHM Ms.Barkha Malay Joshi M.E. Computer Science and Engineering, Parul Institute Of Engineering & Technology, Waghodia. India Email:

### Supervised Learning (Big Data Analytics)

Supervised Learning (Big Data Analytics) Vibhav Gogate Department of Computer Science The University of Texas at Dallas Practical advice Goal of Big Data Analytics Uncover patterns in Data. Can be used

### 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,

### not possible or was possible at a high cost for collecting the data.

Data Mining and Knowledge Discovery Generating knowledge from data Knowledge Discovery Data Mining White Paper Organizations collect a vast amount of data in the process of carrying out their day-to-day

### 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

### 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

### Lecture 3: Linear methods for classification

Lecture 3: Linear methods for classification Rafael A. Irizarry and Hector Corrada Bravo February, 2010 Today we describe four specific algorithms useful for classification problems: linear regression,