1. Ch. Eick: Introduction Data Mining and Course Information 1 Introduction --- Part2 1. Another Introduction to Data Mining 2. Course Information

2. Ch. Eick: Introduction Data Mining and Course Information 2 Knowledge Discovery in Data [and Data Mining] (KDD) Let us find something interesting! Definition := “KDD is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data” (Fayyad) Frequently, the term data mining is used to refer to KDD. Many commercial and experimental tools and tool suites are available (see http://www.kdnuggets.com/siftware.html)http://www.kdnuggets.com/siftware.html Field is more dominated by industry than by research institutions

3. Ch. Eick: Introduction Data Mining and Course Information 3 Motivation: “Necessity is the Mother of Invention” Data explosion problem Automated data collection tools and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories We are drowning in data, but starving for knowledge! Solution: Data warehousing and data mining Data warehousing and on-line analytical processing (“analyzing and mining the raw data rarely works”) —idea: mine summarized,. aggregated data Extraction of interesting knowledge (rules, regularities, patterns, constraints) from data collections

4. Ch. Eick: Introduction Data Mining and Course Information 4 ACME CORP ULTIMATE DATA MINING BROWSER What’s New?What’s Interesting? Predict for me YAHOO!’s View of Data Mining http://www.sigkdd.org/kdd2008/

5. Ch. Eick: Introduction Data Mining and Course Information 5 Data Mining: A KDD Process Data mining: the core of knowledge discovery process. Data Cleaning Data Integration Databases Data Warehouse Task-relevant Data Selection Data Mining Pattern Evaluation

6. Ch. Eick: Introduction Data Mining and Course Information 6 Steps of a KDD Process Learning the application domain: relevant prior knowledge and goals of application Creating a target data set: data selection Data cleaning and preprocessing: Data reduction and transformation (the first 4 steps may take 75% of effort!) : Find useful features, dimensionality/variable reduction, invariant representation. Choosing functions of data mining summarization, classification, regression, association, clustering. Choosing the mining algorithm(s) Data mining: search for patterns of interest Pattern evaluation and knowledge presentation visualization, transformation, removing redundant patterns, etc. Use of discovered knowledge

7. Ch. Eick: Introduction Data Mining and Course Information 7 Data Mining and Business Intelligence Increasing potential to support business decisions End User Business Analyst Data Analyst DBA Making Decisions Data Presentation Visualization Techniques Data Mining Information Discovery Data Exploration OLAP, MDA Statistical Analysis, Querying and Reporting Data Warehouses / Data Marts Data Sources Paper, Files, Information Providers, Database Systems, OLTP

8. Ch. Eick: Introduction Data Mining and Course Information 8 Are All the “Discovered” Patterns Interesting? A data mining system/query 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.

9. Ch. Eick: Introduction Data Mining and Course Information 9 Data Mining: Confluence of Multiple Disciplines Data Mining Machine Learning Statistics Applications Algorithm Pattern Recognition High-Performance Computing Visualization Database Technology

10. 10 KDD Process: A Typical View from ML and Statistics Input Data Data Mining Data Pre- Processing Post- Processing This is a view from typical machine learning and statistics communities Data integration Normalization Feature selection Dimension reduction Association Analysis Classification Clustering Outlier analysis Summary Generation … Pattern evaluation Pattern selection Pattern interpretation Pattern visualization

11. Ch. Eick: Introduction Data Mining and Course Information 11 Data Mining Competitions Netflix Price: http://www.netflixprize.com//index http://www.netflixprize.com//index KDD Cup 2009: http://www.kddcup- orange.com /http://www.kddcup- orange.com / KDD Cup 2011: http://www.kdd.org/kdd2011/kddcup.shtml http://www.kdd.org/kdd2011/kddcup.shtml

12. Ch. Eick: Introduction Data Mining and Course Information 12 Summary Data mining: discovering interesting patterns from large amounts of data A natural evolution of database technology, in great demand, with wide applications A KDD process includes data cleaning, data integration, data selection, transformation, data mining, pattern evaluation, and knowledge presentation Mining can be performed in a variety of information repositories Data mining functionalities: characterization, discrimination, association, classification, clustering, outlier and trend analysis, etc. Classification of data mining systems

13. Ch. Eick: Introduction Data Mining and Course Information COSC 6335 in a Nutshell 13 Preprocessing Data Mining Post Processing Association Analysis Pattern Evaluation Clustering Visualization Summarization Classification & Prediction

14. Ch. Eick: Introduction Data Mining and Course Information 14 Prerequisites The course is basically self contained; however, the following skills are important to be successful in taking this course: Basic knowledge of programming Programming languages of your own choice and data mining tools, particularly R, will be used in the programming projects Basic knowledge of statistics Basic knowledge of data structures

15. Ch. Eick: Introduction Data Mining and Course Information Course Objectives will know what the goals and objectives of data mining are will have a basic understanding on how to conduct a data mining project will obtain practical experience in data analysis and making sense out of data will have sound knowledge of popular classification techniques, such as decision trees, support vector machines and nearest-neighbor approaches. will know the most important association analysis techniques will have detailed knowledge of popular clustering algorithms, such as K- means, DBSCAN, grid-based, hierarchical and supervised clustering. will have sound knowledge of R, an open source statistics/data mining environment will obtain practical experience in designing data mining algorithms and in applying data mining techniques to real world data sets will have some exposure to more advanced topics, such as sequence mining, spatial data mining, and web page ranking algorithms 15

16. Ch. Eick: Introduction Data Mining and Course Information 16 Order of Coverage Introduction  Exploratory Data Analysis  Similarity Assessment  Clustering  Association Analysis  Classification  Spatial Data Mining  More on Classification  OLAP and Data Warehousing  Preprocessing  More on Clustering  Sequence and Graph Mining  Top 10 Data Mining Algorithms  Summary Also: Introductory tutorial into R on Sept. 4, 2014

17. Ch. Eick: Introduction Data Mining and Course Information 17 In particular, R will be used for most course projects, The bad news is that it is more challenging to get started with R (compared to Weka---but Weka is a "dead" language), although you should be okay after you used R for some weeks. On the other hand, the good news about R is that it continues to grow quickly in popularity. A recent poll at KDnuggets found that 34% of respondents do at least half of their data mining in R. Although it's a domain specific language, it's versatile. As we have not used R in the course before, we expect some startup problems and ask you for your patience, but, on the positive side knowing R will be a plus when conducting research projects and when looking for jobs after you graduate, due to R's completeness and R's rising popularity.

18. Ch. Eick: Introduction Data Mining and Course Information 18 Where to Find References? Data mining and KDD Conference proceedings: ICDM, KDD, PKDD, PAKDD, SDM,ADMA etc. Journal: Data Mining and Knowledge Discovery Database field (SIGMOD member CD ROM): Conference proceedings: VLDB, ICDE, ACM-SIGMOD, CIKM Journals: ACM-TODS, J. ACM, IEEE-TKDE, JIIS, etc. AI and Machine Learning: Conference proceedings: ICML, AAAI, IJCAI, ECML, etc. Journals: Machine Learning, Artificial Intelligence, etc. Statistics: Conference proceedings: Joint Stat. Meeting, etc. Journals: Annals of statistics, etc. Visualization: Conference proceedings: CHI, etc. Journals: IEEE Trans. visualization and computer graphics, etc.

19. Ch. Eick: Introduction Data Mining and Course Information 19 Textbooks Required Text: P.-N. Tang, M. Steinback, and V. Kumar: Introduction to Data Mining, Addison Wesley, Link to Book HomePageLink to Book HomePage Mildly Recommended Text Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, Morgan Kaufman Publishers, second edition. Link to Data Mining Book Home Page

20. Ch. Eick: Introduction Data Mining and Course Information 20 Tentative Schedule for 2014 Exams: TBDL, December 11 30 minute Reviews (see webpage): Plan First Half of the Fall 2014 Semester: Aug. 26+28: Introduction to DM / Course Information September 2: Exploratory Data Analysis September 4: R-Lab / Project1 September 9+11+16+23: Clustering I September 18: Background Knowledge Project2 September 25+30 Oct. 2: Association Analysisc October 7: Catchup October 9+14+16: Classification and Prediction I October 21: Spatial Data Mining …

21. Ch. Eick: Introduction Data Mining and Course Information 21 2014 Course Projects Project 1: Exploratory Data Analysis Project 2: Traditional Clustering with K-means and DBSCAN and Interpreting Clustering Results Individual Project Project 3: Group Project (centering on Association Analysis) Project 4: Reading and Summarizing Data Mining Papers Workload: Project 3 medium sized; 1+4 short;

22. Ch. Eick: Introduction Data Mining and Course Information 22 Teaching Assistant: Arko Barman Duties: 1. Grading of programming projects, home works, and exams (in part) 2. Teach R-Lab and 1-2 Lectures 3. Help students with homework, programming projects and problems with the course material 4. Teach a class (once; could be also other students of my research group) Office: Office Hours: TU 2-3p TH 3-4p E-mail: Meet our TA: Thursday, August 28

23. Ch. Eick: Introduction Data Mining and Course Information 23 Web and News Group Course Webpage (http://www2.cs.uh.edu/~ceick/DM/DM.html )http://www2.cs.uh.edu/~ceick/DM/DM.html UH-DMML Webpage (http://www2.cs.uh.edu/~UH-DMML/index.html)http://www2.cs.uh.edu/~UH-DMML/index.html Arko will set up COSC 6335 News Group

24. Ch. Eick: Introduction Data Mining and Course Information 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.

25. Ch. Eick: Introduction Data Mining and Course Information 25 Teaching Philosophy and Advice The first 9 weeks will give a basic introduction to data mining and follows the textbook somewhat closely. Read the sections of the textbook before you come to the lecture; if you work continuously for the class you will do better and lectures will be more enjoyable. Starting to review the material that is covered in this class 1 week before the next exam is not a good idea. Do not be afraid to ask questions! I really like interactions with students in the lectures… If you do not understand something at all send me an e-mail before the next lecture! If you have a serious problem talk to me, before the problem gets out of hand.

26. Ch. Eick: Introduction Data Mining and Course Information 26 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.

27. Ch. Eick: Introduction Data Mining and Course Information 27 Course Planning for Research in Data Mining This course “Data Mining” I also suggest to taking at least 1, preferably two, of the following courses: Artificial Intelligence (COSC 6368), and Machine Learning (COSC 6342). Moreover, having basic knowledge in data structures, software design, and databases is important when conducting data mining projects; therefore, taking COSC 6320, COSC 6318 and COSC 6340 is a good choice. Also Dr. Guoning Chen’s visualization course is very useful for data mining. Moreover, taking a course that teaches high performance computing is also a good choice. Because a lot of data mining projects have to deal with images, I suggest to take at least one of the many biomedical image processing courses that are offered in our curriculum. Finally, having knowledge in evolutionary computing, solving optimization problems, GIS (geographical information systems) is a plus!