1. CENG 514

2. 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 Alternative names – Knowledge discovery (mining) in databases (KDD), knowledge extraction, data/pattern analysis, data archeology, data dredging, information harvesting, business intelligence, etc.

3. Definition by Gartner Group “Data mining is the process of discovering meaningful new correlations, patterns and trends by sifting through large amounts of data stored in repositories, using pattern recognition technologies as well as statistical and mathematical techniques.”

4. (Deductive) query processing Expert systems or small ML/statistical programs

5. Lots of data is being collected and warehoused – Web data, e-commerce – purchases at department/ grocery stores – Bank/Credit Card transactions Computers have become cheaper and more powerful Competitive Pressure is Strong – Provide better, customized services for an edge (e.g. in Customer Relationship Management) Why Mine Data? Commercial Viewpoint

6. Why Mine Data? Scientific Viewpoint Data collected and stored at enormous speeds (GB/hour) – remote sensors on a satellite – telescopes scanning the skies – microarrays generating gene expression data – scientific simulations generating terabytes of data Traditional techniques infeasible for raw data Data mining may help scientists – in classifying and segmenting data – in Hypothesis Formation

7. Mining Large Data Sets - Motivation There is often information “ hidden ” in the data that is not readily evident Human analysts may take weeks to discover useful information Much of the data is never analyzed at all The Data Gap Total new disk (TB) since 1995 Number of analysts From: R. Grossman, C. Kamath, V. Kumar, “Data Mining for Scientific and Engineering Applications”

8. Machine Learning Database Management Artificial Intelligence Statistics Data Mining Visualization Algorithms

9. 9 Data Mining: History of the Field Knowledge Discovery in Databases workshops started ‘89 – Now a conference under the auspices of ACM SIGKDD – IEEE conference series started 2001

10. CS490D10 A Brief History of Data Mining Society 1989 IJCAI Workshop on Knowledge Discovery in Databases (Piatetsky- Shapiro) – Knowledge Discovery in Databases (G. Piatetsky-Shapiro and W. Frawley, 1991) 1991-1994 Workshops on Knowledge Discovery in Databases – Advances in Knowledge Discovery and Data Mining (U. Fayyad, G. Piatetsky- Shapiro, P. Smyth, and R. Uthurusamy, 1996) 1995-1998 International Conferences on Knowledge Discovery in Databases and Data Mining (KDD’95-98) – Journal of Data Mining and Knowledge Discovery (1997) 1998 ACM SIGKDD, SIGKDD’1999-2001 conferences, and SIGKDD Explorations More conferences on data mining – PAKDD (1997), PKDD (1997), SIAM-Data Mining (2001), (IEEE) ICDM (2001), etc.

11. Market Analysis, Customer Relationships Management (CRM) Churn Analysis Risk Analysis and Management Fraud Detection, Counter Terrorism Network Intrusion Detection Web Site Restructring Recommendation Scientific Applications

12. 12 Applications: Corporate Analysis & Risk Management Finance planning and asset evaluation – cash flow analysis and prediction – contingent claim analysis to evaluate assets – cross-sectional and time series analysis (financial-ratio, trend analysis, etc.) Resource planning – summarize and compare the resources and spending Competition – monitor competitors and market directions – group customers into classes and a class-based pricing procedure – set pricing strategy in a highly competitive market

13. 13 Applications: Fraud Detection & Mining Unusual Patterns Approaches: Clustering & model construction for frauds, outlier analysis Applications: Health care, retail, credit card service, telecomm. – Auto insurance: ring of collisions – Money laundering: suspicious monetary transactions – Medical insurance Professional patients, ring of doctors, and ring of references Unnecessary or correlated screening tests – Telecommunications: phone-call fraud Phone call model: destination of the call, duration, time of day or week. Analyze patterns that deviate from an expected norm – Anti-terrorism

14. 14 Example: Use in retailing Goal: Improved business efficiency – Improve marketing (advertise to the most likely buyers) – Inventory reduction (stock only needed quantities) Information source: Historical business data – Example: Supermarket sales records – Size ranges from 50k records (research studies) to terabytes (years of data from chains) – Data is already being warehoused Sample question – what products are generally purchased together? The answers are in the data, if only we could see them

15. 15 Example: Churn Analysis Business Problem: Prevent loss of customers, avoid adding churn-prone customers Solution: Use neural nets, time series analysis to identify typical patterns of telephone usage of likely- to-defect and likely-to-churn customers Benefit: Retention of customers, more effective promotions

16. 16 Example: Clicks to Customers Business problem: 50% of Dell’s clients order their computer through the web. However, the retention rate is 0.5%, i.e. of visitors of Dell’s web page become customers. Solution Approach: Through the sequence of their clicks, cluster customers and design website, interventions to maximize the number of customers who eventually buy. Benefit: Increase revenues

17. 17 What Can Data Mining Do? Cluster Classify – Categorical, Regression Summarize – Summary statistics, Summary rules Link Analysis / Model Dependencies – Association rules Sequence analysis – Time-series analysis, Sequential associations Detect Deviations

18. 18 Clustering Find groups of similar data items Statistical techniques require some definition of “distance” (e.g. between travel profiles) while conceptual techniques use background concepts and logical descriptions “Group people with similar travel profiles” – George, Patricia – Jeff, Evelyn, Chris – Rob

19. 19 Classification Find ways to separate data items into pre-defined groups Requires “training data”: Data items where group is known “Route documents to most likely interested parties” – English or non-english? – Domestic or Foreign?

20. 20 Association Rules Identify dependencies in the data: – X makes Y likely Indicate significance of each dependency “Find groups of items commonly purchased together” – People who purchase fish are extraordinarily likely to purchase wine – People who purchase Turkey are extraordinarily likely to purchase cranberries

21. 21 Sequential Associations Find event sequences that are unusually likely “Find common sequences of warnings/faults within 10 minute periods” – Warn 2 on Switch C preceded by Fault 21 on Switch B – Fault 17 on any switch preceded by Warn 2 on any switch

22. 22 Recommendation Techniques Given database of user preferences, predict preference of new user Example: – Predict what new movies you will like based on your past preferences others with similar past preferences their preferences for the new movies – Predict what books/CDs a person may want to buy (and suggest it, or give discounts to tempt customer)

23. 23 adapted from: U. Fayyad, et al. (1995), “From Knowledge Discovery to Data Mining: An Overview,” Advances in Knowledge Discovery and Data Mining, U. Fayyad et al. (Eds.), AAAI/MIT Press Data Target Data Selection Knowledge Preprocessed Data Patterns Data Mining Interpretation/ Evaluation Knowledge Discovery in Databases: Process Preprocessing

24. 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

25. Learning the application domain – relevant prior knowledge and goals of application Creating a target data set: data selection Data cleaning and preprocessing: (may take 60% of effort!) Data reduction and transformation – 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

26. 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 – Data Quality: 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

27. What is Data? Collection of data objects and their attributes An attribute is a property or characteristic of an object – Examples: eye color of a person, temperature, etc. – Attribute is also known as variable, field, characteristic, or feature A collection of attributes describe an object – Object is also known as record, point, case, sample, entity, or instance Attributes Objects

28. Attribute Values Attribute values are numbers or symbols assigned to an attribute Distinction between attributes and attribute values – Same attribute can be mapped to different attribute values Example: height can be measured in feet or meters – Different attributes can be mapped to the same set of values Example: Attribute values for ID and age are integers But properties of attribute values can be different – ID has no limit but age has a maximum and minimum value

29. Types of data sets Record – Data Matrix – Document Data – Transaction Data Graph – World Wide Web – Molecular Structures Ordered – Spatial Data – Temporal Data – Sequential Data – Genetic Sequence Data

30. Record Data Data that consists of a collection of records, each of which consists of a fixed set of attributes

31. Data Matrix If data objects have the same fixed set of numeric attributes, then the data objects can be thought of as points in a multi- dimensional space, where each dimension represents a distinct attribute Such data set can be represented by an m by n matrix, where there are m rows, one for each object, and n columns, one for each attribute

32. Document Data Each document becomes a `term' vector, – each term is a component (attribute) of the vector, – the value of each component is the number of times the corresponding term occurs in the document.

33. Transaction Data A special type of record data, where – each record (transaction) involves a set of items. – For example, consider a grocery store. The set of products purchased by a customer during one shopping trip constitute a transaction, while the individual products that were purchased are the items.

34. Graph Data Examples: Generic graph and HTML Links

35. Ordered Data Sequences of transactions An element of the sequence Items/Events

36. Discrete and Continuous Attributes Discrete Attribute – Has only a finite or countably infinite set of values – Examples: zip codes, counts, or the set of words in a collection of documents – Often represented as integer variables. – Note: binary attributes are a special case of discrete attributes Continuous Attribute – Has real numbers as attribute values – Examples: temperature, height, or weight. – Practically, real values can only be measured and represented using a finite number of digits. – Continuous attributes are typically represented as floating-point variables.

37. Important Characteristics of Structured Data – Dimensionality Curse of Dimensionality – Sparsity Only presence counts – Resolution Patterns depend on the scale