Unit 1.A.Introduction to Knowledge Discovery Data Mining (1).pptx
- 2. KDD: A Definition • KDD is the automatic extraction of non-obvious, hidden knowledge from large volumes of data. 106 -1012 bytes: we never see the whole data set, so will put it in the memory of computers What is the knowledge? How to represent and use it? Then run Data Mining algorithms
- 3. Wal-Mart records 20 millions per day Why do we need KDD ? Data Overload Science Marketing Finance Healthcare Retail Health care transactions: multi-gigabyte databases Mobil Oil: geological data of over 100 terabytes Some Data Overload Examples: Data is the most Important tool to gain a competitive edge by providing improved, customized services.
- 4. Knowledge Discovery Process __ __ __ __ __ __ __ __ __ Transformed Data Pattern s and Rules Target Data Raw Data Knowledge Data Mining Transformation Interpretation & Evaluation Selection & Cleaning Integratio n Understandin g DATA Ware house Knowledg e
- 5. Knowledge Discovery in Database • Knowledge discovery in databases (KDD) is the non-trivial process of identifying valid, potentially useful and ultimately understandable patterns in data Clean, Collect, Summarize Data Warehouse Data Preparation Training Data Data Mining Model Patterns Verification, Evaluation Operational Databases
- 6. Knowledge Discovery Process Goals Data Selection, Acquisition & Integration Data Cleaning Data Reduction & Projection Matching the Goals Exploratory Data Analysis Data Mining Interpretation and Testing Consolidation & Use
- 7. Knowledge Discovery Process • First step is developing an understanding of the application domain and the relevant prior knowledge and identifying the goal of the KDD process from the customer’s viewpoint. STEP – 1: IDENTIFYING THE GOAL • Goals • Data Selection, Acquisition & Integration • Data Cleaning • Data reduction and Projection •Matching the goals • Exploratory Data Analysis • Data Mining •Interpretation and Testing • Consolidation & Use
- 8. Knowledge Discovery Process • Selecting a data set, or focusing on a subset of variables or data samples, on which discovery is to be performed. STEP – 2: CREATING A TARGET DATA SET • Goals • Data Selection, Acquisition & Integration • Data Cleaning • Data reduction and Projection •Matching the goals • Exploratory Data Analysis • Data Mining •Interpretation and Testing • Consolidation & Use
- 9. Knowledge Discovery Process • Basic operations include removing noise if appropriate, collecting the necessary information to model or account for noise, deciding on strategies for handling missing data fields, and accounting for time- sequence information and known changes. STEP – 3: DATA CLEANING AND PREPROCESSING • Goals • Data Selection, Acquisition & Integration • Data Cleaning • Data reduction and Projection •Matching the goals • Exploratory Data Analysis • Data Mining •Interpretation and Testing • Consolidation & Use
- 10. Knowledge Discovery Process • Finding useful features to represent the data depending on the goal of the task. • With dimensionality reduction or transformation methods, the effective number of variables under consideration can be reduced, or invariant representations for the data can be found. • Goals • Data Selection, Acquisition & Integration • Data Cleaning • Data reduction and Projection •Matching the goals • Exploratory Data Analysis • Data Mining •Interpretation and Testing • Consolidation & Use STEP – 4: DATA REDUCTION AND PROJECTION
- 11. Knowledge Discovery Process • Matching the goals of the KDD process to a particular data-mining method such as summarization, classification, regression, clustering, etc. STEP – 5: MATCHING THE GOALS • Goals • Data Selection, Acquisition & Integration • Data Cleaning • Data reduction and Projection •Matching the goals • Exploratory Data Analysis • Data Mining •Interpretation and Testing • Consolidation & Use
- 12. Knowledge Discovery Process • Choosing the data mining algorithms and selecting methods to be used for searching for data patterns. • This process includes deciding which models and parameters might be appropriate and matching a particular data-mining method with the overall criteria of the KDD process. STEP – 6: EXPLORATORY ANALYSIS AND MODEL & HYPOTHESIS SELECTION • Goals • Data Selection, Acquisition & Integration • Data Cleaning • Data reduction and Projection •Matching the goals • Exploratory Data Analysis • Data Mining • Interpretation and Testing • Consolidation & Use
- 13. Knowledge Discovery Process • Searching for patterns of interest in a particular representational form or a set of such representations, including classification rules or trees, regression, and clustering. • The user can significantly aid the data-mining method by correctly performing the preceding steps. STEP – 7: DATA MINING • Goals • Data Selection, Acquisition & Integration • Data Cleaning • Data reduction and Projection •Matching the goals • Exploratory Data Analysis • Data Mining •Interpretation and Testing • Consolidation & Use
- 14. Knowledge Discovery Process • Interpreting mined patterns, possibly returning to any of steps 1 through 7 for further iteration. • This step can also involve visualization of the extracted patterns and models or visualization of the data given the extracted models. STEP – 8: INTERPRETATION & TESTING • Goals • Data Selection, Acquisition & Integration • Data Cleaning • Data reduction and Projection •Matching the goals • Exploratory Data Analysis • Data Mining •Interpretation and Testing • Consolidation & Use
- 15. Knowledge Discovery Process • Using the knowledge directly, incorporating the knowledge into another system for further action, or simply documenting it and reporting it to interested parties. • This process also includes checking for and resolving potential conflicts with previously believed (or extracted) knowledge. STEP – 9: KNOWLEDGE PRESENTATION • Goals • Data Selection, Acquisition & Integration • Data Cleaning • Data reduction and Projection •Matching the goals • Exploratory Data Analysis • Data Mining • Testing and Verification • Interpretation • Consolidation & Use
- 16. Data Warehousing • A platform for online analytical processing (OLAP) • Warehouses collect transactional data from several transactional databases and organize them in a fashion amenable to analysis • Also called “data marts” • A critical component of the decision support system (DSS) of enterprises • Some typical DW queries: – Which item sells best in each region that has retail outlets? – Which advertising strategy is best for Dubai Markets?
- 17. Data Warehousing Order Processing Inventory Sales Data Cleaning Data Warehouse (OLAP) OLTP (online analytical processing)
- 18. Data Cleaning • Performs logical transformation of transactional data to suit the data warehouse • Model of operations model of enterprise 🡺 • Usually a semi-automatic process Orders Order_id Price Cust_id Inventory Prod_id Price Price_change Sales Cust_id Cust_profit Total_sales Data Warehouse Customers Products Orders Inventory Price Time
- 19. Primary Tasks of Data Mining Classification Deviation and change detection Summarization Clustering Dependency Modeling Regression finding the description of several predefined classes and classify a data item into one of them. maps a data item to a real-valued prediction variable. identifying a finite set of categories or clusters to describe the data. finding a compact description for a subset of data finding a model which describes significant dependencies between variables. discovering the most significant changes in the data
- 20. Data Mining Algorithm Components • Model representation – descriptions of discovered patterns – overly limited representation -- unable to capture data patterns too powerful -- potential for over fit. (decision trees, rules, linear/non-linear regression & classification, nearest neighbor and case-based reasoning methods, graphical dependency models) • Model evaluation criteria – how well a pattern (model) meets goals (fit function) – e.g., accuracy, novelty, etc.
- 21. Data Mining Algorithm Components • Search method – parameter search: optimization of parameters for a given model representation – model search: considers a family of models Different methods suit different problems. Proper problem formulation crucial.
- 22. Data Mining Techniques Data Mining Techniques Descriptive Predictive Clustering Association Classification Regression Sequential Analysis Decision Tree Rule Induction Neural Networks Nearest Neighbor Classification
- 23. Association Rule: Application • Supermarket Shelf Management • Goal: to identify items which are bought together (by sufficiently many customers) • Approach: process point-of-sale data (collected with barcode scanners) to find dependencies among items. • Consider discovered rule: {Diapers, Milk … } --> {Baby food} • Example: – If a customer buys Diapers and Milk, then he is very likely to buy Baby foods. – so stack baby foods next to diapers?
- 24. Sequential Pattern Discovery: Application • Sequences in which customers purchase goods/services • Understanding long term customer behavior -- timely promotions. • In point-of--sale transaction sequences – Computer bookstore: (Intro to Visual C++) (Java & J2EE) --> (Perl for Dummies, PHP in 24 Hrs) – Athletic Apparel Store: (Shoes) (Racket, Racket ball) --> (Sports Jacket)
- 25. Hierarchical Clustering (K-Means): Application 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 0 1 2 3 4 5 6 7 8 9 10 K=2 Arbitrarily choose K objects as initial cluster center Assign each of the objects to most similar center Update the cluster means Update the cluster means reassign Hierarchical clustering: Clusters are formed at different levels by merging clusters at a lower level
- 26. Decision Tree Identification: Application Outlook Temp Play? Sunny Warm Yes Overcast Chilly No Sunny Chilly Yes Cloudy Pleasant Yes Overcast Pleasant Yes Overcast Chilly No Cloudy Chilly No Cloudy Warm Yes Sunny Cloudy Overcast Yes Yes/No Yes/No Decision Tree Identification Example
- 27. Decision Tree Identification: Application Yes/No Yes/No Yes Yes/No Sunny Cloudy Overcast Yes No Yes No Yes Warm Chilly Pleasant Chilly Pleasant
- 28. Major Application Areas for Data Mining (Classification) • Advertising • Bioinformatics • Customer Relationship Management (CRM) • Database Marketing • Fraud Detection • ecommerce • Health Care • Investment/Securities • Manufacturing, Process Control • Sports and Entertainment • Telecommunications • Web
- 29. Major Application Areas for Data Mining: Marketing • Direct Marketing: Most major direct marketing companies are using modeling and data mining. • Customer segmentation: All industries can take advantage of DM to discover discrete segments in their customer bases by considering additional variables beyond traditional analysis. • CRM: Find other people in similar life stages and determine which customers are following similar behavior patterns – Up-sell – Cross-sell – Keeping the customers for a longer period of time
- 30. Major Application Areas for Data Mining: Fraud Detection • Credit Card Fraud Detection • Money laundering – FAIS (US Treasury) • Securities Fraud – NASDAQ Sonar system • Phone fraud – AT&T, Bell Atlantic, British Telecom/MCI • Bio-terrorism detection at Salt Lake Olympics 2002
- 31. Major Application Areas for Data Mining: Retail • Sales forecasting: Examining time-based patterns helps retailers make stocking decisions. • Database Retailing: Retailers can develop profiles of customers with certain behaviors, for example, those who purchase designer labels clothing or those who attend sales. • Merchandise planning and allocation: When retailers add new stores, they can improve merchandise planning and allocation by examining patterns in stores with similar demographic characteristics.
- 32. Major Application Areas for Data Mining: Banking • Credit Card marketing By identifying customer segments, card issuers and acquirers can improve profitability with more effective acquisition and retention programs. • Cardholder pricing and profitability Card issuers can take advantage of data mining technology to price their products so as to maximize profit and minimize loss of customers.
- 33. Major Application Areas for Data Mining: Telecommunication • Call detail record analysis: Telecommunication companies accumulate detailed call records. By identifying customer segments with similar use patterns, the companies can develop attractive pricing and feature promotions. • Customer loyalty: Some customers repeatedly switch providers, or “churn”, to take advantage of attractive incentives by competing companies. The companies can use DM to identify the characteristics of customers who are likely to remain loyal once they switch, thus enabling the companies to target their spending on customers who will produce the most profit.
- 34. Major Application Areas for Data Mining: Manufacturing • Manufacturing: Through choice boards, manufacturers are beginning to customize products for customers; therefore they must be able to predict which features should be bundled to meet customer demand. • Warranties: Manufacturers need to predict the number of customers who will submit warranty claims and the average cost of those claims.
- 35. Issues and Challenges • Large data – Number of variables (features), number of cases (examples) – Multi gigabyte, terabyte databases – Efficient algorithms, parallel processing • High dimensionality – Large number of features: exponential increase in search space – Potential for spurious patterns – Dimensionality reduction • Over fitting – Models noise in training data, rather than just the general patterns • Changing data, missing and noisy data • Use of domain knowledge – Utilizing knowledge on complex data relationships, known facts • Understandability of patterns