Data Mining: Concepts and Techniques
|
|
|
- Alban Payne
- 7 years ago
- Views:
Transcription
1 Data Mining: Concepts and Techniques Conclusions: DM Basics, Issues and Trends Hui Yang Department of Computer Science San Francisco State University May 12, 2008 Data Mining: Concepts and Techniques 1
2 Why Data Mining? The Explosive Growth of Data: from terabytes to petabytes Data collection and data availability Automated data collection tools, database systems, Web, computerized society Major sources of abundant data Business: Web, e-commerce, transactions, stocks, Science: Remote sensing, bioinformatics, scientific simulation, Society and everyone: news, digital cameras, YouTube We are drowning in data, but starving for knowledge! Necessity is the mother of invention Data mining Automated analysis of massive data sets May 12, 2008 Data Mining: Concepts and Techniques 2
3 What Is Data Mining? Data mining (knowledge discovery from data) Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data Data mining: a misnomer? Alternative names Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc. Watch out: Is everything data mining? Simple search and query processing (Deductive) expert systems May 12, 2008 Data Mining: Concepts and Techniques 3
4 Knowledge Discovery (KDD) Process Data mining core of knowledge discovery process Data Mining Pattern Evaluation Task-relevant Data Data Warehouse Selection Data Cleaning Data Integration Databases May 12, 2008 Data Mining: Concepts and Techniques 4
5 Data Mining and Business Intelligence Increasing potential to support business decisions Decision Making Data Presentation Visualization Techniques Data Mining Information Discovery End User Business Analyst Data Analyst Data Exploration Statistical Summary, Querying, and Reporting Data Preprocessing/Integration, Data Warehouses Data Sources Paper, Files, Web documents, Scientific experiments, Database Systems DBA May 12, 2008 Data Mining: Concepts and Techniques 5
6 Data Mining: Confluence of Multiple Disciplines Database Technology Statistics Machine Learning Data Mining Visualization Pattern Recognition Algorithm Other Disciplines May 12, 2008 Data Mining: Concepts and Techniques 6
7 Architecture: Typical Data Mining System Graphical User Interface Pattern Evaluation Data Mining Engine Knowl edge- Base Database or Data Warehouse Server data cleaning, integration, and selection Database Data Warehouse World-Wide Web Other Info Repositories May 12, 2008 Data Mining: Concepts and Techniques 7
8 Multi-Dimensional View of Data Mining Data to be mined Relational, data warehouse, transactional, stream, objectoriented/relational, active, spatial, time-series, text, multi-media, heterogeneous, legacy, WWW Knowledge to be mined Characterization, discrimination, association, classification, clustering, trend/deviation, outlier analysis, etc. Multiple/integrated functions and mining at multiple levels Techniques utilized Database-oriented, data warehouse (OLAP), machine learning, statistics, visualization, etc. Applications adapted Retail, telecommunication, banking, fraud analysis, bio-data mining, stock market analysis, text mining, Web mining, etc. May 12, 2008 Data Mining: Concepts and Techniques 8
9 Data Mining: On What Kinds of Data? Database-oriented data sets and applications Relational database, data warehouse, transactional database Advanced data sets and advanced applications Data streams and sensor data Time-series data, temporal data, sequence data (incl. bio-sequences) Structure data, graphs, social networks and multi-linked data Object-relational databases Heterogeneous databases and legacy databases Spatial data and spatiotemporal data Multimedia database Text databases The World-Wide Web May 12, 2008 Data Mining: Concepts and Techniques 9
10 Why Data Mining? Potential Applications Data analysis and decision support Market analysis and management Target marketing, customer relationship management (CRM), market basket analysis, cross selling, market segmentation Risk analysis and management Forecasting, customer retention, improved underwriting, quality control, competitive analysis Fraud detection and detection of unusual patterns (outliers) Other Applications Text mining (news group, , documents) and Web mining Stream data mining Bioinformatics and bio-data analysis May 12, 2008 Data Mining: Concepts and Techniques 10
11 Data Mining Functionalities Multidimensional concept description: Characterization and discrimination Generalize, summarize, and contrast data characteristics, e.g., dry vs. wet regions Frequent patterns, association, correlation vs. causality Diaper Beer [0.5%, 75%] (Correlation or causality?) Classification and prediction Construct models (functions) that describe and distinguish classes or concepts for future prediction E.g., classify countries based on (climate), or classify cars based on (gas mileage) Predict some unknown or missing numerical values May 12, 2008 Data Mining: Concepts and Techniques 11
12 Data Mining Functionalities (2) Cluster analysis Class label is unknown: Group data to form new classes, e.g., cluster houses to find distribution patterns Maximizing intra-class similarity & minimizing interclass similarity Outlier analysis Outlier: Data object that does not comply with the general behavior of the data Noise or exception? Useful in fraud detection, rare events analysis Trend and evolution analysis Trend and deviation: e.g., regression analysis Sequential pattern mining: e.g., digital camera large SD memory Periodicity analysis Similarity-based analysis Other pattern-directed or statistical analyses May 12, 2008 Data Mining: Concepts and Techniques 12
13 Top-10 Most Popular DM Algorithms: 18 Identified Candidates (I) Classification #1. C4.5: Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann., #2. CART: L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth, #3. K Nearest Neighbours (knn): Hastie, T. and Tibshirani, R Discriminant Adaptive Nearest Neighbor Classification. TPAMI. 18(6) #4. Naive Bayes Hand, D.J., Yu, K., Idiot's Bayes: Not So Stupid After All? Internat. Statist. Rev. 69, Statistical Learning #5. SVM: Vapnik, V. N The Nature of Statistical Learning Theory. Springer-Verlag. #6. EM: McLachlan, G. and Peel, D. (2000). Finite Mixture Models. J. Wiley, New York. Association Analysis #7. Apriori: Rakesh Agrawal and Ramakrishnan Srikant. Fast Algorithms for Mining Association Rules. In VLDB '94. #8. FP-Tree: Han, J., Pei, J., and Yin, Y Mining frequent patterns without candidate generation. In SIGMOD '00. May 12, 2008 Data Mining: Concepts and Techniques 13
14 The 18 Identified Candidates (II) Link Mining #9. PageRank: Brin, S. and Page, L The anatomy of a large-scale hypertextual Web search engine. In WWW-7, #10. HITS: Kleinberg, J. M Authoritative sources in a hyperlinked environment. SODA, Clustering #11. K-Means: MacQueen, J. B., Some methods for classification and analysis of multivariate observations, in Proc. 5th Berkeley Symp. Mathematical Statistics and Probability, #12. BIRCH: Zhang, T., Ramakrishnan, R., and Livny, M BIRCH: an efficient data clustering method for very large databases. In SIGMOD '96. Bagging and Boosting #13. AdaBoost: Freund, Y. and Schapire, R. E A decisiontheoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55, 1 (Aug. 1997), May 12, 2008 Data Mining: Concepts and Techniques 14
15 The 18 Identified Candidates (III) Sequential Patterns #14. GSP: Srikant, R. and Agrawal, R Mining Sequential Patterns: Generalizations and Performance Improvements. In Proceedings of the 5th International Conference on Extending Database Technology, #15. PrefixSpan: J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal and M-C. Hsu. PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In ICDE '01. Integrated Mining #16. CBA: Liu, B., Hsu, W. and Ma, Y. M. Integrating classification and association rule mining. KDD-98. Rough Sets #17. Finding reduct: Zdzislaw Pawlak, Rough Sets: Theoretical Aspects of Reasoning about Data, Kluwer Academic Publishers, Norwell, MA, 1992 Graph Mining #18. gspan: Yan, X. and Han, J gspan: Graph-Based Substructure Pattern Mining. In ICDM '02. May 12, 2008 Data Mining: Concepts and Techniques 15
16 Top-10 Algorithm Finally Selected at ICDM 06 #1: C4.5 (61 votes) #2: K-Means (60 votes) #3: SVM (58 votes) #4: Apriori (52 votes) #5: EM (48 votes) #6: PageRank (46 votes) #7: AdaBoost (45 votes) #7: knn (45 votes) #7: Naive Bayes (45 votes) #10: CART (34 votes) May 12, 2008 Data Mining: Concepts and Techniques 16
17 Are All the Discovered Patterns Interesting? Data mining may generate thousands of patterns: Not all of them are interesting Suggested approach: Human-centered, query-based, focused mining Interestingness measures A pattern is interesting if it is easily understood by humans, valid on new or test data with some degree of certainty, potentially useful, novel, or validates some hypothesis that a user seeks to confirm Objective vs. subjective interestingness measures Objective: based on statistics and structures of patterns, e.g., support, confidence, etc. Subjective: based on user s belief in the data, e.g., unexpectedness, novelty, actionability, etc. May 12, 2008 Data Mining: Concepts and Techniques 17
18 Major Issues in Data Mining Mining methodology Mining different kinds of knowledge from diverse data types, e.g., bio, stream, Web Performance: efficiency, effectiveness, and scalability Pattern evaluation: the interestingness problem Incorporation of background knowledge Handling noise and incomplete data Parallel, distributed and incremental mining methods Integration of the discovered knowledge with existing one: knowledge fusion User interaction Data mining query languages and ad-hoc mining Expression and visualization of data mining results Interactive mining of knowledge at multiple levels of abstraction Applications and social impacts Domain-specific data mining & invisible data mining Protection of data security, integrity, and privacy May 12, 2008 Data Mining: Concepts and Techniques 18
19 Data Mining Languages & Standardization Efforts Association rule language specifications MSQL (Imielinski & Virmani 99) MineRule (Meo Psaila and Ceri 96) Query flocks based on Datalog syntax (Tsur et al 98) OLEDB for DM (Microsoft 2000) and recently DMX (Microsoft SQLServer 2005) Based on OLE, OLE DB, OLE DB for OLAP, C# Integrating DBMS, data warehouse and data mining DMML (Data Mining Mark-up Language) by DMG ( Providing a platform and process structure for effective data mining Emphasizing on deploying data mining technology to solve business problems May 12, 2008 Data Mining: Concepts and Techniques 19
20 Social Impacts: Threat to Privacy and Data Security? Is data mining a threat to privacy and data security? Big Brother, Big Banker, and Big Business are carefully watching you Profiling information is collected every time credit card, debit card, supermarket loyalty card, or frequent flyer card, or apply for any of the above You surf the Web, rent a video, fill out a contest entry form, You pay for prescription drugs, or present you medical care number when visiting the doctor Collection of personal data may be beneficial for companies and consumers, there is also potential for misuse Medical Records, Employee Evaluations, etc. May 12, 2008 Data Mining: Concepts and Techniques 20
21 Protect Privacy and Data Security Fair information practices International guidelines for data privacy protection Cover aspects relating to data collection, purpose, use, quality, openness, individual participation, and accountability Purpose specification and use limitation Openness: Individuals have the right to know what information is collected about them, who has access to the data, and how the data are being used Develop and use data security-enhancing techniques Blind signatures Biometric encryption Anonymous databases May 12, 2008 Data Mining: Concepts and Techniques 21
22 Trends in Data Mining (1) Application exploration Development of application-specific data mining system Invisible data mining (mining as built-in function) Scalable data mining methods Constraint-based mining: use of constraints to guide data mining systems in their search for interesting patterns Integration of data mining with database systems, data warehouse systems, and Web database systems Invisible data mining May 12, 2008 Data Mining: Concepts and Techniques 22
23 Trends in Data Mining (2) Standardization of data mining language A standard will facilitate systematic development, improve interoperability, and promote the education and use of data mining systems in industry and society Visual data mining New methods for mining complex types of data More research is required towards the integration of data mining methods with existing data analysis techniques for the complex types of data Web mining Privacy protection and information security in data mining May 12, 2008 Data Mining: Concepts and Techniques 23
24 Where to Find References? DBLP, CiteSeer, Google Data mining and KDD (SIGKDD: CDROM) Conferences: ACM-SIGKDD, IEEE-ICDM, SIAM-DM, PKDD, PAKDD, etc. Journal: Data Mining and Knowledge Discovery, KDD Explorations, ACM TKDD Database systems (SIGMOD: ACM SIGMOD Anthology CD ROM) Conferences: ACM-SIGMOD, ACM-PODS, VLDB, IEEE-ICDE, EDBT, ICDT, DASFAA Journals: IEEE-TKDE, ACM-TODS/TOIS, JIIS, J. ACM, VLDB J., Info. Sys., etc. AI & Machine Learning Conferences: Machine learning (ML), AAAI, IJCAI, COLT (Learning Theory), CVPR, NIPS, etc. Journals: Machine Learning, Artificial Intelligence, Knowledge and Information Systems, IEEE-PAMI, etc. Web and IR Conferences: SIGIR, WWW, CIKM, etc. Journals: WWW: Internet and Web Information Systems, Statistics Conferences: Joint Stat. Meeting, etc. Journals: Annals of statistics, etc. Visualization Conference proceedings: CHI, ACM-SIGGraph, etc. Journals: IEEE Trans. visualization and computer graphics, etc. May 12, 2008 Data Mining: Concepts and Techniques 24
25 Recommended Reference Books S. Chakrabarti. Mining the Web: Statistical Analysis of Hypertex and Semi-Structured Data. Morgan Kaufmann, 2002 R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2ed., Wiley-Interscience, 2000 T. Dasu and T. Johnson. Exploratory Data Mining and Data Cleaning. John Wiley & Sons, 2003 U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996 U. Fayyad, G. Grinstein, and A. Wierse, Information Visualization in Data Mining and Knowledge Discovery, Morgan Kaufmann, 2001 J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann, 2 nd ed., 2006 D. J. Hand, H. Mannila, and P. Smyth, Principles of Data Mining, MIT Press, 2001 T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer-Verlag, 2001 B. Liu, Web Data Mining, Springer T. M. Mitchell, Machine Learning, McGraw Hill, 1997 G. Piatetsky-Shapiro and W. J. Frawley. Knowledge Discovery in Databases. AAAI/MIT Press, 1991 P.-N. Tan, M. Steinbach and V. Kumar, Introduction to Data Mining, Wiley, 2005 S. M. Weiss and N. Indurkhya, Predictive Data Mining, Morgan Kaufmann, 1998 I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann, 2 nd ed May 12, 2008 Data Mining: Concepts and Techniques 25
Top 10 Algorithms in Data Mining
Top 10 Algorithms in Data Mining Xindong Wu ( 吴 信 东 ) Department of Computer Science University of Vermont, USA; 合 肥 工 业 大 学 计 算 机 与 信 息 学 院 1 Top 10 Algorithms in Data Mining by the IEEE ICDM Conference
Top Top 10 Algorithms in Data Mining
ICDM 06 Panel on Top Top 10 Algorithms in Data Mining 1. The 3-step identification process 2. The 18 identified candidates 3. Algorithm presentations 4. Top 10 algorithms: summary 5. Open discussions ICDM
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques Chapter 1 Introduction SURESH BABU M ASST PROF IT DEPT VJIT 1 Chapter 1. Introduction Motivation: Why data mining? What is data mining? Data Mining: On what kind of
Data Mining: Concepts and Techniques
Slides related to: Data Mining: Concepts and Techniques Chapter 1 and 2 Introduction and Data preprocessing Jiawei Han and Micheline Kamber Department of Computer Science University of Illinois at Urbana-Champaign
Data Mining: Concepts and Techniques. (3 rd ed.) Chapter 1
Data Mining: Concepts and Techniques (3 rd ed.) Chapter 1 Jiawei Han, Micheline Kamber, and Jian Pei University of Illinois at Urbana-Champaign & Simon Fraser University 2013 Han, Kamber & Pei. All rights
Introduction. What is Data Mining?
Introduction What is Data Mining? Data Mining: Concepts and Techniques Slides for Course Data Mining Chapter 1 Jiawei Han Necessity Is the Mother of Invention Data explosion problem Automated data collection
Data Mining: Concepts and Techniques
1 Data Mining: Concepts and Techniques Chapter 1 Introduction Jiawei Han and Micheline Kamber Department of Computer Science University of Illinois at Urbana-Champaign www.cs.uiuc.edu/~hanj 2006 Jiawei
Introduction to Data Mining
Bioinformatics Ying Liu, Ph.D. Laboratory for Bioinformatics University of Texas at Dallas Spring 2008 Introduction to Data Mining 1 Motivation: Why data mining? What is data mining? Data Mining: On what
Introduction. A. Bellaachia Page: 1
Introduction 1. Objectives... 3 2. What is Data Mining?... 4 3. Knowledge Discovery Process... 5 4. KD Process Example... 7 5. Typical Data Mining Architecture... 8 6. Database vs. Data Mining... 9 7.
Massive Data Analytics
1 Massive Data Analytics Data Mining Introduction Themis Palpanas University of Trento http://disi.unitn.eu/~themis Data Mining for Knowledge Management 1 Thanks for slides to: Jiawei Han Jeff Ullman Data
Introduction to Data Mining
Introduction to Data Mining 1 Why Data Mining? Explosive Growth of Data Data collection and data availability Automated data collection tools, Internet, smartphones, Major sources of abundant data Business:
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)
Data Mining: Concepts and Techniques Chapter 1 Introduction
Data Mining: Concepts and Techniques Chapter 1 Introduction October 25, 2013 Data Mining: Concepts and Techniques 1 Chapter 1. Introduction Motivation: Why data mining? What is data mining? Data Mining:
What is Data Mining?
Introduction What is Data Mining? Data Mining: Concepts and Techniques Slides for Course Data Mining Chapter 1 Jiawei Han 1 Necessity Is the Mother of Invention Data explosion problem Automated data collection
Data Warehousing and Data Mining
Data Warehousing and Data Mining Winter Semester 2010/2011 Free University of Bozen, Bolzano DW Lecturer: Johann Gamper gamper@inf.unibz.it DM Lecturer: Mouna Kacimi mouna.kacimi@unibz.it http://www.inf.unibz.it/dis/teaching/dwdm/index.html
Data Mining Introduction
Data Mining Introduction Organization Lectures Mondays and Thursdays from 10:30 to 12:30 Lecturer: Mouna Kacimi Office hours: appointment by email Labs Thursdays from 14:00 to 16:00 Teaching Assistant:
Data Warehousing and Data Mining
Data Warehousing and Data Mining Winter Semester 2012/2013 Free University of Bozen, Bolzano DM Lecturer: Mouna Kacimi mouna.kacimi@unibz.it http://www.inf.unibz.it/dis/teaching/dwdm/index.html Organization
Knowledge Discovery Process and Data Mining - Final remarks
Knowledge Discovery Process and Data Mining - Final remarks Lecturer: JERZY STEFANOWSKI Institute of Computing Sciences Poznan University of Technology Poznan, Poland Lecture 14 SE Master Course 2008/2009
Marshall University Syllabus Course Title/Number Data Mining / CS 515 Semester/Year Fall / 2015 Days/Time Tu, Th 9:30 10:45 a.m. Location GH 211 Instructor Hyoil Han Office Gullickson Hall 205B Phone (304)696-6204
Example application (1) Telecommunication. Lecture 1: Data Mining Overview and Process. Example application (2) Health
Lecture 1: Data Mining Overview and Process What is data mining? Example applications Definitions Multi disciplinary Techniques Major challenges The data mining process History of data mining Data mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques Chapter 11 Applications and Trends in Data Mining SURESH BABU M ASST PROFESSOR VJIT 1 Applications and Trends in Data Mining Data mining applications Data mining system
Data Mining: Concepts and Techniques. Jiawei Han. Micheline Kamber. Simon Fräser University К MORGAN KAUFMANN PUBLISHERS. AN IMPRINT OF Elsevier
Data Mining: Concepts and Techniques Jiawei Han Micheline Kamber Simon Fräser University К MORGAN KAUFMANN PUBLISHERS AN IMPRINT OF Elsevier Contents Foreword Preface xix vii Chapter I Introduction I I.
International Journal of Scientific & Engineering Research, Volume 5, Issue 4, April-2014 442 ISSN 2229-5518
International Journal of Scientific & Engineering Research, Volume 5, Issue 4, April-2014 442 Over viewing issues of data mining with highlights of data warehousing Rushabh H. Baldaniya, Prof H.J.Baldaniya,
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
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
Data Mining. Introduction to Modern Information Retrieval from Databases and the Web. Administrivia
Administrivia Data Mining Introduction to Modern Information Retrieval from Databases and the Web Instructor: Kostis Sagonas (MIC, Hus 1, 352) Course home page: http://user.it.uu.se/~kostis/teaching/dm-05/
Data Mining System, Functionalities and Applications: A Radical Review
Data Mining System, Functionalities and Applications: A Radical Review Dr. Poonam Chaudhary System Programmer, Kurukshetra University, Kurukshetra Abstract: Data Mining is the process of locating potentially
131-1. Adding New Level in KDD to Make the Web Usage Mining More Efficient. Abstract. 1. Introduction [1]. 1/10
![131-1. Adding New Level in KDD to Make the Web Usage Mining More Efficient. Abstract. 1. Introduction [1]. 1/10](/thumbs/25/4963058.jpg "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
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
Data Mining: Concepts and Techniques (2nd edition)
Data Mining: Concepts and Techniques (2nd edition) Jiawei Han and Micheline Kamber Morgan Kaufmann Publishers, 2006 Bibliographic Notes for Chapter 1: Introduction The book Knowledge Discovery in Databases,
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
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
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
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. Knowledge Discovery, Data Warehousing and Machine Learning Final remarks. Lecturer: JERZY STEFANOWSKI
Data Mining Knowledge Discovery, Data Warehousing and Machine Learning Final remarks Lecturer: JERZY STEFANOWSKI Email: Jerzy.Stefanowski@cs.put.poznan.pl Data Mining a step in A KDD Process Data mining:
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
DATA MINING CONCEPTS AND TECHNIQUES. Marek Maurizio E-commerce, winter 2011
DATA MINING CONCEPTS AND TECHNIQUES Marek Maurizio E-commerce, winter 2011 INTRODUCTION Overview of data mining Emphasis is placed on basic data mining concepts Techniques for uncovering interesting data
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
Introduction. Chapter 1. 1.1 Bibliographic Notes
Chapter 1 Introduction 1.1 Bibliographic Notes The book Knowledge Discovery in Databases, edited by Piatetsky-Shapiro and Frawley [PSF91], is an early collection of research papers on knowledge discovery
How To Use Data Mining For Knowledge Management In Technology Enhanced Learning
Proceedings of the 6th WSEAS International Conference on Applications of Electrical Engineering, Istanbul, Turkey, May 27-29, 2007 115 Data Mining for Knowledge Management in Technology Enhanced Learning
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
Web Mining Seminar CSE 450. Spring 2008 MWF 11:10 12:00pm Maginnes 113
CSE 450 Web Mining Seminar Spring 2008 MWF 11:10 12:00pm Maginnes 113 Instructor: Dr. Brian D. Davison Dept. of Computer Science & Engineering Lehigh University davison@cse.lehigh.edu http://www.cse.lehigh.edu/~brian/course/webmining/
Introduction to Data Mining and Business Intelligence Lecture 1/DMBI/IKI83403T/MTI/UI
Introduction to Data Mining and Business Intelligence Lecture 1/DMBI/IKI83403T/MTI/UI Yudho Giri Sucahyo, Ph.D, CISA (yudho@cs.ui.ac.id) Faculty of Computer Science, University of Indonesia Objectives
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
Introduction to Data Mining
Introduction to Data Mining Jay Urbain Credits: Nazli Goharian & David Grossman @ IIT Outline Introduction Data Pre-processing Data Mining Algorithms Naïve Bayes Decision Tree Neural Network Association
Introduction to Data Mining and Machine Learning Techniques. Iza Moise, Evangelos Pournaras, Dirk Helbing
Introduction to Data Mining and Machine Learning Techniques Iza Moise, Evangelos Pournaras, Dirk Helbing Iza Moise, Evangelos Pournaras, Dirk Helbing 1 Overview Main principles of data mining Definition
The Role of Visualization in Effective Data Cleaning
The Role of Visualization in Effective Data Cleaning Yu Qian Dept. of Computer Science The University of Texas at Dallas Richardson, TX 75083-0688, USA qianyu@student.utdallas.edu Kang Zhang Dept. of Computer
Mining Sequence Data. JERZY STEFANOWSKI Inst. Informatyki PP Wersja dla TPD 2009 Zaawansowana eksploracja danych
Mining Sequence Data JERZY STEFANOWSKI Inst. Informatyki PP Wersja dla TPD 2009 Zaawansowana eksploracja danych Outline of the presentation 1. Realtionships to mining frequent items 2. Motivations for
Mining an Online Auctions Data Warehouse
Proceedings of MASPLAS'02 The Mid-Atlantic Student Workshop on Programming Languages and Systems Pace University, April 19, 2002 Mining an Online Auctions Data Warehouse David Ulmer Under the guidance
International Journal of World Research, Vol: I Issue XIII, December 2008, Print ISSN: 2347-937X DATA MINING TECHNIQUES AND STOCK MARKET
DATA MINING TECHNIQUES AND STOCK MARKET Mr. Rahul Thakkar, Lecturer and HOD, Naran Lala College of Professional & Applied Sciences, Navsari ABSTRACT Without trading in a stock market we can t understand
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
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
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
Selection of Optimal Discount of Retail Assortments with Data Mining Approach
Available online at www.interscience.in Selection of Optimal Discount of Retail Assortments with Data Mining Approach Padmalatha Eddla, Ravinder Reddy, Mamatha Computer Science Department,CBIT, Gandipet,Hyderabad,A.P,India.
Concept and Applications of Data Mining. Week 1
Concept and Applications of Data Mining Week 1 Topics Introduction Syllabus Data Mining Concepts Team Organization Introduction Session Your name and major The dfiiti definition of dt data mining i Your
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.:
How To Use Data Mining For Loyalty Based Management
Data Mining for Loyalty Based Management Petra Hunziker, Andreas Maier, Alex Nippe, Markus Tresch, Douglas Weers, Peter Zemp Credit Suisse P.O. Box 100, CH - 8070 Zurich, Switzerland markus.tresch@credit-suisse.ch,
ECLT 5810 E-Commerce Data Mining Techniques - Introduction. Prof. Wai Lam
ECLT 5810 E-Commerce Data Mining Techniques - Introduction Prof. Wai Lam Data Opportunities Business infrastructure have improved the ability to collect data Virtually every aspect of business is now open
Data Mining is sometimes referred to as KDD and DM and KDD tend to be used as synonyms
Data Mining Techniques forcrm Data Mining The non-trivial extraction of novel, implicit, and actionable knowledge from large datasets. Extremely large datasets Discovery of the non-obvious Useful knowledge
CS490D: Introduction to Data Mining Chris Clifton. What Is Data Mining?
CS490D: Introduction to Data Mining Chris Clifton January 12, 2004 Course Overview What Is Data Mining? Data mining (knowledge discovery from data) Extraction of interesting (non-trivial, implicit, previously
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
Knowledge Discovery from Databases
Encyclopedia of Database Technologies and Applications Knowledge Discovery from Databases Jose Hernandez-Orallo Dep. of Information Systems and Computation Technical University of Valencia, Spain jorallo@dsic.upv.es
MINING CLICKSTREAM-BASED DATA CUBES
MINING CLICKSTREAM-BASED DATA CUBES Ronnie Alves and Orlando Belo Departament of Informatics,School of Engineering, University of Minho Campus de Gualtar, 4710-057 Braga, Portugal Email: {alvesrco,obelo}@di.uminho.pt
Data Mining: Opportunities and Challenges
Data Mining: Opportunities and Challenges Xindong Wu University of Vermont, USA; Hefei University of Technology, China ( 合 肥 工 业 大 学 计 算 机 应 用 长 江 学 者 讲 座 教 授 ) 1 Deduction Induction: My Research Background
How To Write An Association Rules Mining For Business Intelligence
International Journal of Scientific and Research Publications, Volume 4, Issue 5, May 2014 1 Association Rules Mining for Business Intelligence Rashmi Jha NIELIT Center, Under Ministry of IT, New Delhi,
Subject Description Form
Subject Description Form Subject Code Subject Title COMP417 Data Warehousing and Data Mining Techniques in Business and Commerce Credit Value 3 Level 4 Pre-requisite / Co-requisite/ Exclusion Objectives
Data Mining for Fun and Profit
Data Mining for Fun and Profit Data mining is the extraction of implicit, previously unknown, and potentially useful information from data. - Ian H. Witten, Data Mining: Practical Machine Learning Tools
INTRODUCTION TO KNOWLEDGE DISCOVERY IN DATABASES
Chapter 1 INTRODUCTION TO KNOWLEDGE DISCOVERY IN DATABASES Oded Maimon Department of Industrial Engineering Tel-Aviv University maimon@eng.tau.ac.il Lior Rokach Department of Industrial Engineering Tel-Aviv
Data Warehousing and Data Mining in Business Applications
133 Data Warehousing and Data Mining in Business Applications Eesha Goel CSE Deptt. GZS-PTU Campus, Bathinda. Abstract Information technology is now required in all aspect of our lives that helps in business
Mining Online GIS for Crime Rate and Models based on Frequent Pattern Analysis
, 23-25 October, 2013, San Francisco, USA Mining Online GIS for Crime Rate and Models based on Frequent Pattern Analysis John David Elijah Sandig, Ruby Mae Somoba, Ma. Beth Concepcion and Bobby D. Gerardo,
Overview of Data Mining
1. Introduction The fast developing computer science and engineering techniques has made the information easy to capture process and store in databases. Information/data are considered as elementary variable
A Review of Data Mining Techniques
Available Online at www.ijcsmc.com International Journal of Computer Science and Mobile Computing A Monthly Journal of Computer Science and Information Technology IJCSMC, Vol. 3, Issue. 4, April 2014,
A RESEARCH STUDY ON DATA MINING TECHNIQUES AND ALGORTHMS
A RESEARCH STUDY ON DATA MINING TECHNIQUES AND ALGORTHMS Nitin Trivedi, Research Scholar, Manav Bharti University, Solan HP ABSTRACT The purpose of this study is not to delve deeply into the technical
Data Mining course Master in Information Technologies Enginyeria Informàtica Tomàs Aluja. LIAM EIO. UPC Lluis Belanche LSI. UPC
Data Mining course Master in Information Technologies Enginyeria Informàtica Tomàs Aluja. LIAM EIO. UPC Lluis Belanche LSI. UPC Topics Introduction to Data Mining Preprocess Finding profiles Visualisation
FREQUENT PATTERN MINING FOR EFFICIENT LIBRARY MANAGEMENT
FREQUENT PATTERN MINING FOR EFFICIENT LIBRARY MANAGEMENT ANURADHA.T Assoc.prof, atadiparty@yahoo.co.in SRI SAI KRISHNA.A saikrishna.gjc@gmail.com SATYATEJ.K satyatej.koganti@gmail.com NAGA ANIL KUMAR.G
How To Solve The Kd Cup 2010 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 - 1DL105, 1Dl111
1 DATA MINING - 1DL105, 1Dl111 Fall 2006 An introductory class in data mining http://www.it.uu.se/edu/course/homepage/infoutv/ht06 Kjell Orsborn Uppsala Database Laboratory Department of Information Technology,
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,
Data Mining as an Automated Service
Data Mining as an Automated Service P. S. Bradley Apollo Data Technologies, LLC paul@apollodatatech.com February 16, 2003 Abstract An automated data mining service offers an out- sourced, costeffective
Data Mining to Recognize Fail Parts in Manufacturing Process
122 ECTI TRANSACTIONS ON ELECTRICAL ENG., ELECTRONICS, AND COMMUNICATIONS VOL.7, NO.2 August 2009 Data Mining to Recognize Fail Parts in Manufacturing Process Wanida Kanarkard 1, Danaipong Chetchotsak
Random forest algorithm in big data environment
Random forest algorithm in big data environment Yingchun Liu * School of Economics and Management, Beihang University, Beijing 100191, China Received 1 September 2014, www.cmnt.lv Abstract Random forest
AMIS 7640 Data Mining for Business Intelligence
The Ohio State University The Max M. Fisher College of Business Department of Accounting and Management Information Systems AMIS 7640 Data Mining for Business Intelligence Autumn Semester 2013, Session
Database Marketing, Business Intelligence and Knowledge Discovery
Database Marketing, Business Intelligence and Knowledge Discovery Note: Using material from Tan / Steinbach / Kumar (2005) Introduction to Data Mining,, Addison Wesley; and Cios / Pedrycz / Swiniarski
Subjective Measures and their Role in Data Mining Process
Subjective Measures and their Role in Data Mining Process Ahmed Sultan Al-Hegami Department of Computer Science University of Delhi Delhi-110007 INDIA ahmed@csdu.org Abstract Knowledge Discovery in Databases
Introduction to Data Mining
Chapter I: Introduction to Data Mining We are in an age often referred to as the information age. In this information age, because we believe that information leads to power and success, and thanks to
Mining On-line Newspaper Web Access Logs
Mining On-line Newspaper Web Access Logs Paulo Batista, Mário J. Silva Departamento de Informática Faculdade de Ciências Universidade de Lisboa Campo Grande 1700 Lisboa, Portugal {pb, mjs} @di.fc.ul.pt
AN EFFICIENT SELECTIVE DATA MINING ALGORITHM FOR BIG DATA ANALYTICS THROUGH HADOOP
AN EFFICIENT SELECTIVE DATA MINING ALGORITHM FOR BIG DATA ANALYTICS THROUGH HADOOP Asst.Prof Mr. M.I Peter Shiyam,M.E * Department of Computer Science and Engineering, DMI Engineering college, Aralvaimozhi.
Data Mining. Vera Goebel. Department of Informatics, University of Oslo
Data Mining Vera Goebel Department of Informatics, University of Oslo 2011 1 Lecture Contents Knowledge Discovery in Databases (KDD) Definition and Applications OLAP Architectures for OLAP and KDD KDD
Chapter 2 Literature Review
Chapter 2 Literature Review 2.1 Data Mining The amount of data continues to grow at an enormous rate even though the data stores are already vast. The primary challenge is how to make the database a competitive
Healthcare Measurement Analysis Using Data mining Techniques
www.ijecs.in International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 03 Issue 07 July, 2014 Page No. 7058-7064 Healthcare Measurement Analysis Using Data mining Techniques 1 Dr.A.Shaik
Machine Learning, Data Mining, and Knowledge Discovery: An Introduction
Machine Learning, Data Mining, and Knowledge Discovery: An Introduction AHPCRC Workshop - 8/17/10 - Dr. Martin Based on slides by Gregory Piatetsky-Shapiro from Kdnuggets http://www.kdnuggets.com/data_mining_course/
Syllabus. HMI 7437: Data Warehousing and Data/Text Mining for Healthcare
Syllabus HMI 7437: Data Warehousing and Data/Text Mining for Healthcare 1. Instructor Illhoi Yoo, Ph.D Office: 404 Clark Hall Email: muteaching@gmail.com Office hours: TBA Classroom: TBA Class hours: TBA
An Overview of Database management System, Data warehousing and Data Mining
An Overview of Database management System, Data warehousing and Data Mining Ramandeep Kaur 1, Amanpreet Kaur 2, Sarabjeet Kaur 3, Amandeep Kaur 4, Ranbir Kaur 5 Assistant Prof., Deptt. Of Computer Science,
Data Mining - Introduction
Data Mining - Introduction Peter Brezany Institut für Scientific Computing Universität Wien Tel. 4277 39425 Sprechstunde: Di, 13.00-14.00 Outline Business Intelligence and its components Knowledge discovery
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
DATA MINING AND WAREHOUSING CONCEPTS
CHAPTER 1 DATA MINING AND WAREHOUSING CONCEPTS 1.1 INTRODUCTION The past couple of decades have seen a dramatic increase in the amount of information or data being stored in electronic format. This accumulation
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
Gold. Mining for Information
Mining for Information Gold Data mining offers the RIM professional an opportunity to contribute to knowledge discovery in databases in a substantial way Joseph M. Firestone, Ph.D. During the late 1980s,
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
ANALYSIS OF WEBSITE USAGE WITH USER DETAILS USING DATA MINING PATTERN RECOGNITION
ANALYSIS OF WEBSITE USAGE WITH USER DETAILS USING DATA MINING PATTERN RECOGNITION K.Vinodkumar 1, Kathiresan.V 2, Divya.K 3 1 MPhil scholar, RVS College of Arts and Science, Coimbatore, India. 2 HOD, Dr.SNS