CSECBS 572 Data Mining Huan Liu CSE CEAS

CSE/CBS 572 Data Mining Huan Liu, CSE, CEAS, ASU http: //www. public. asu. edu/~huanliu/DM 07 S/cse 572. html 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 1

CSE 572 Contents of basic and advanced topics Classification, Clustering, Association, and Applications Format – An interactive and hands-on course with ample opportunities to work, create and share Paper reading, discussion, project, presentation, or any learning activities you can suggest Assessment in various ways Class participation, assignments, quizzes, a course project, presentations, 1 or 2 exams 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 2

You: our future data mining star, a potential zillionaire TA: Lei Tang, l. tang@asu. edu Me: Huan Liu, huanliu@asu. edu n n Where: Brickyard 566 When: see on the course website, or by appointment “No pain, no gain”, or “As you sow, so you shall reap”. We will also learn the principle of “No Free Lunch”. My. ASU will be used, make sure won’t miss important announcement and your email address stays current (I push some information to you via emails sometimes) 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 3

Course Format What is the effective teaching of graduate data mining ? Your feedback is keenly sought. Current research papers - the main categories to be found on the course web site. You can choose one of the textbooks listed. It is an entering point for you to access related subjects. The truth is It is a fast changing field. Everyone is expected to read research papers and participate in class discussion. Paper presentations. Project presentations. Presentations will also be evaluated in class. 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 4

Point distribution (tentative) Projects (20 -25%) Reading/presentation assignment (5%) Exam(s) (50%) Assignments (20 -25%), and class participation, quizzes (up to 10% extra credit) Late penalty, YES, increasing exponentially. Academic integrity (http: //www. public. asu. edu/~huanliu/conduct. html) 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 5

Research paper reading We will provide a reading list and you can also choose your favorite All are expected to search for and read the selected papers – part of the learning process. n n n What is it about (e. g. , key idea, basic algorithm)? What are points to discuss and improve? What can we do with it? What to submit? (see more on the class website) n n n A brief report that describes the above and 2 questions suitable for quizzes/tests with solutions A set of presentation slides for 20 minutes Due date: TBA, use digital drop box Grading criteria include (1) quality of additional papers you select, (2) slides for presentation, (3) the report, and (4) oral presentation will be selected among the best submissions and presenters will be given extra credit based on presentation Presentation can start as early as in Feburary, if possible. 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 6

Project Proposal n A theme-based project for all in class? w More discussion later n n Proposal presentation, discussion, revision A project worth the effort of a semester’s work Progress report Final report n Class presentation and/or demo One key goal of this course is to take advantage of your intelligence and (limited) experience (so you’re bold and creative) to expand your knowledge in creating something useful and interesting 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 7

Topic Distribution (tentative) 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 8

Categories of interests (including design and implementation) 1. Data and application security Data mining and privacy 2. Data reduction and selection Streaming data reduction Dealing with large data (column- & row-wise) Search bias, overfitting 3. Learning algorithms Ensemble methods Semi-supervised learning Active learning and co-training 4. Bioinformatics or others w 3/3/2021 A discussion board will be created, if needed CSE/CBS 572: Data Mining by Huan Liu 9

Your first assignment is to think 1. Think about what you want to accomplish in this course. 2. List 2 of your areas of interests (don’t be restricted by the previous list, and this is one of some rare opportunities that allow you to day-dream and earn grade points), and briefly justify your choices. 3. Pick an area of interests and choose a general topic for paper presentation. Submission is in hardcopy n 4. Complete the above and submit it in the 3 rd class (Monday, January 29, 2007). 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 10

Your 2 nd homework (project related) assignment Find some (2 -3) Web 2. 0 or Health. Care applications n n Wikipedia, some examples: facebook, del. icio. us, myspace, digg, … Massive data of various types in a hospital environment Surf the Web and compile their URLs and functions Choose your favorite one and explain why n How can data mining help in your perspective with your limited knowledge of data mining Test-drive it (manually, imagine that you have what you wish for, …) Write a summary about your experience, challenges you encountered, and suggestions if any Due on Wednesday February 7, 2007 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 11

Selecting a paper of your interest First, continue from your 1 st (and/or 2 nd) homework assignments about your category of interest Second, you may form presentation groups (e. g. , 2 -3 a group for discussion purposes) Third, each student picks a paper from the given category and find 1 additional high-quality relevant paper n n n Submit it through my. ASU The first student in each category will present the given paper (s/he does not need to look for another paper) TA will organize and help you and compile a list of all papers at the end Write a summary for your selected paper including n n n What is it about Why is it significant and relevant Where is it published and when 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 12

Introduction The need for data mining in the Internet era n Data everywhere, examples? Data mining n n Text mining Image mining Web mining (log, link, content, blog) Bioinformatics Many products and abundant applications n Where do we stand 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 13

What is data mining Data mining is n n extraction of useful patterns from data sources, e. g. , databases, texts, web, image. 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. 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 14

Patterns (1) Patterns are the relationships and summaries derived through a data mining exercise. Patterns must be: n n valid novel potentially useful, or actionable understandable 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 15

Patterns (2) Patterns are used for n n n prediction or classification describing the existing data segmenting the data (e. g. , the market) profiling the data (e. g. , your customers) Detection (e. g. , intrusion, fault, anomaly) 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 16

Data (1) Data mining typically deals with data that have already been collected for some purpose other than data mining. Data miners usually have no influence on data collection strategies. Large bodies of data cause new problems: representation, storage, retrieval, analysis, . . . 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 17

Data (2) Even with a very large data set, we are usually faced with just a sample from the population. Data exist in many types (continuous, nominal) and forms (credit card usage records, supermarket transactions, government statistics, text, images, medical records, human genome databases, molecular databases). 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 18

Typical DM tasks Classification: n mining patterns that can classify future data into known classes. Association rule mining: n mining any rule of the form X Y, where X and Y are sets of data items. Clustering: n identifying a set of similar groups in the data 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 19

Sequential pattern mining: A sequential rule: A B, says that event A will be immediately followed by event B with a certain confidence Deviation/anomaly/exception detection: discovering the most significant changes in data Data visualization (or visual analytics): using graphical methods to show patterns in data. High performance computing Bioinformatics 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 20

Why data mining Rapid computerization of businesses produces huge amounts of data How to make best use of data to our advantages? A growing realization: knowledge discovered from data can be used for competitive advantage and to increase business intelligence. There are problems that might not be suitable for data mining – Top 10 Statistics Problems for Capital. One (Bill Khan’s invited talk at SIGKDD’ 06) 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 21

Make use/sense of your data assets Many interesting things you want to find cannot be found using database queries “find me people likely to buy my products” “Who are likely to respond to my promotion” Fast identify underlying relationships and respond to emerging opportunities 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 22

Why now and for the near future The data is abundant. The data is being collected or warehoused. The computing power is affordable. The competitive pressure is increasing. Data mining tools have become available. New challenges n n New data types evolve New applications emerge 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 23

DM fields Data mining is an emerging multidisciplinary field: Statistics Machine learning Databases Visualization OLAP and data warehousing High-performance computing. . . 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 24

Summary What is data mining? KDD - knowledge discovery in databases: non-trivial extraction of implicit, previously unknown and potentially useful/actionable information Why do we need data mining? Wide use of computer systems - data explosion knowledge is power – but we’re data rich, knowledge poor – useful, understandable and actionable knowledge. . . Data mining is not a plug-and-play, so we are not done yet and need to continue this class … 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 25

An Overview of KDD Process (Guess which is which) 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 26

Web mining – an application The Web is a massive database Semi-structured data XML and RDF Web mining n n 3/3/2021 Content Structure Usage Link analysis CSE/CBS 572: Data Mining by Huan Liu 27

About project Data collection n How, where, what to collect Evaluation methods n How and why Analysis and mining n n What requires our (yet-to-develop) expertise For what Potentially useful methods n For example, graph theories, social networks Great applications 3/3/2021 CSE/CBS 572: Data Mining by Huan Liu 28