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Data Mining: Introduction Lecture Notes for Chapter 1 Introduction to Data Mining

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) | Data Mining: Introduction Lecture Notes for Chapter 1 Introduction to Data Mining by Tan, Steinbach, Kumar 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 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 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 . | Data Mining: Introduction Lecture Notes for Chapter 1 Introduction to Data Mining by Tan, Steinbach, Kumar 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 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 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” What is Data Mining? Many Definitions Non-trivial extraction of implicit, previously unknown and potentially useful information from data Exploration & analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns What is (not) Data Mining? What is Data Mining? Certain names are more prevalent in certain US locations (O’Brien, O’Rurke, O’Reilly in Boston area) Group together similar documents returned by search engine according to their context (e.g. Amazon rainforest, Amazon.com,) What is not Data Mining? Look up phone number in phone directory Query a Web search engine for information about “Amazon” Draws ideas from machine learning/AI, pattern recognition, statistics, and database systems Traditional .