1 / 27

Understanding Data Mining: Exploring Patterns in Large Data Sets

Learn about data mining and its importance in discovering valid, novel, and useful patterns in large quantities of data. Understand how data preprocessing and mining models are used in various applications such as market basket analysis and association rules.

jwilkens
Download Presentation

Understanding Data Mining: Exploring Patterns in Large Data Sets

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chapter 26: Data Mining Prepared by Assoc. Professor Bela Stantic

  2. Definition Data mining is the exploration and analysis of large quantities of data in order to discover valid, novel, potentially useful, and ultimately understandable patterns in data. Example pattern: 62% of customers who bought milk bought cheese as well

  3. Definition (Cont.) Data mining is the exploration and analysis of large quantities of data in order to discover valid, novel, potentially useful, and ultimately understandable patterns in data. Valid: The patterns hold in general. Novel: We did not know the pattern beforehand. Useful: We can devise actions from the patterns. Understandable: We can interpret and comprehend the patterns.

  4. Why Use Data Mining Today? Human analysis skills are inadequate: • Volume and dimensionality of the data • High data growth rate Availability of: • Data • Storage • Computational power • Off-the-shelf software • Expertise

  5. Sources of Data • Supermarket scanners, POS data • Credit card transactions • Direct mail response • Call center records • ATM machines • Demographic data • Sensor networks • Cameras • Web server logs • Customer web site trails

  6. Why Use Data Mining Today? Competitive pressure! “The secret of success is to know something that nobody else knows.” Aristotle Onassis • Competition on service, not only on price (Banks, phone companies, hotel chains, rental car companies) • Personalization, • The real-time enterprise

  7. The Knowledge Discovery Process Steps: • Identify business problem • Data mining • Action • Evaluation and measurement • Deployment and integration into businesses processes

  8. Data Mining Step in Detail 2.1 Data preprocessing • Data selection: Identify target datasets and relevant fields • Data cleaning • Remove noise and outliers • Data transformation • Create common units • Generate new fields 2.2 Data mining model construction 2.3 Model evaluation – present to the end user in understandable form (visually)

  9. Preprocessing and Mining Knowledge Patterns PreprocessedData TargetData Interpretation ModelConstruction Original Data Preprocessing DataIntegrationand Selection

  10. What is a Data Mining Model? A data mining model is a description of a specific aspect of a dataset. It produces output values for an assigned set of input values. Examples: • Linear regression model • Classification model • Clustering

  11. Data Mining: Types of Data • Relational data and transactional data • Spatial and temporal data, spatio-temporal observations • Time-series data • Text • Images, video • Mixtures of data • Sequence data • Features from processing other data sources

  12. Types of Variables • Numerical: Domain is ordered and can be represented on the real line (e.g., age, income) • Nominal or categorical: Domain is a finite set without any natural ordering (e.g., occupation, marital status, race) • Ordinal: Domain is ordered, but absolute differences between values is unknown (e.g., preference scale, severity of an injury)

  13. Applications of Frequent Itemsets • Market Basket Analysis • Association Rules • Classification (especially: text) • Seeds for construction of Bayesian Networks • Web log analysis • Collaborative filtering

  14. Frequent Itemset • Itemset – set of items purchased {pen}, {pen, milk}, … • Support of the itemset is the fraction of transactions in database that contain all the items in the itemset. • Frequent Itemset - If support is higher than the user-specified minimal support • The a Priori property – Every subset of a frequent itemset is also a frequent itemset

  15. Frequent Itemset – refined algorithm • Min support 70% • Level 1 – finds that the items {pen}, {milk}, and {ink} are frequent itemset • Level 2 following the a Priori Property set of two items can be only from frequent itemset: {pen, milk}, {pen, ink} and {ink, milk}. We find that the itemsets {pen, milk}, {pen, ink} are frequent. • Level 3 in not required as item (ink, milk} is not frequent so therefore itemset {pen, ink, milk} is not frequent as well.

  16. Iceberg queries • We can apply logic from the refined frequent itemset algorithm to the Iceberg queries. • Consider example: SELECT custID, Item, sum(qty) FROM Purchase GROUP BY custID, Item HAVIN SUM(qty)> 5 This query would perform better if we look only for customers or items that satisfy the criteria: OR SELECT custID, sum(qty) SELECT Item, sum(qty) FROM Purchase FROM Purchase GROUP BY custID GROUP BY Item HAVIN SUM(qty)> 5 HAVIN SUM(qty)> 5

  17. Association Analysis • Consider shopping cart filled with several items • Market basket analysis tries to answer the following questions: • Who makes purchases? • What do customers buy together? • In what order do customers purchase items? • When do customers purchase the most and what?

  18. Given: A database of customer transactions Each transaction is a set of items Example:Transaction with TID 111 contains items {Pen, Ink, Milk, Juice} Market Basket Analysis

  19. Market Basket Analysis (Contd.) • Coocurrences • 80% of all customers purchase items X, Y and Z together. • Association rules • 60% of all customers who purchase X and Y also buy Z. • Sequential patterns • 60% of customers who first buy X also purchase Y within three weeks.

  20. Confidence and Support We prune the set of all possible association rules using two interestingness measures: • Support of a rule: • X à Y has support s if P(XY) = s • Represents percentage of the transactions that contain all these items • Confidence of a rule: • X à Y has confidence c if P(sup(LHS U RHS) | sup (LHS)) = c • Confidence for a rule X à Y is the percentage of such transactions that also contain all items in Y We can also define • Support of an itemset (a coocurrence) XY: • XY has support s if P(XY) = s

  21. Examples: {Pen} => {Milk}Support: 100%Confidence: 75% {Ink} => {Pen}Support: 75%Confidence: 100% Example

  22. Find all itemsets withsupport >= 75%? Example

  23. Can you find all association rules with support >= 50%? Example

  24. Market Basket Analysis: Applications • Sample Applications • Direct marketing • Fraud detection for medical insurance • Floor/shelf planning • Web site layout • Cross-selling

  25. Association Rules and ISA Hierarchies • Or Category hierarchy, can be imposed on group of items in same hierarchy such as, Pen and Ink belong to Stationary while Juice and milk belong to Beverages. • When applying Assoc. Rules on hiearchy it allows us to detect relationship between different levels of hierarchies.

  26. Generalised Association Rules “On a day when a pen is purchased, it is likely that the milk is also purchased” • If we use the date field as group we can consider more general problem called calendric market basket analysis. • Every Thursday, First Sunday every Month, First Monday every Semester, etc

  27. The use of Assoc. Rules for prediction • Are widely used for prediction, however such predictive usage is not justified without additional analysis and domain knowledge.

More Related