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Data Mining: an Overview Applications, Privacy issues & overview of classification techniques

Explore the process and significance of data mining in uncovering patterns, applied in industries like insurance, telecom, retail, and banking. Discover new developments in genomics, national security, and more. Understand the components and techniques used in this high-value activity.

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Data Mining: an Overview Applications, Privacy issues & overview of classification techniques

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  1. Data Mining: an Overview Applications, Privacy issues & overview of classification techniques DM1- Part 1 Developed by: Dr Eddie Ip Modified by: Dr Arif Ansari

  2. Overview • What is DM: process of exploration and analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns and rules.(Berry & Linoff, 1997) • An art as well as a science (metaphor: photography) • Discover hidden information

  3. Overview • Why DM? • DW provides the Enterprise with a memory • DM provides the Enterprise with intelligence • Why now? • Technical reason: Availability of • Data collected via online & offline transactions, DB vendors, appliances….. • warehousing technology • computing power

  4. Why now? (continued) • Business reason: • competitive pressure, e.g. telecom, retail, finance, insurance • Every business is a service business => understanding customers is more important than ever • Mass customization => CRM • Information as product, e.g. credit card business

  5. Overview • Where is DM applied? • Insurance: cross selling, profiling • Telecom: manage churning, detect fraud • Retail: information broker using DM (e.g. Safeway charged 5.5cents per name, p12 B&L) • Bank: market credit card, detect fraud, CRM (e.g., call center mgt) • Online business: Web tracking

  6. Overview • How? • Style • Directed = top-down (e.g. predictive modeling) • Undirected = bottom-up (e.g. visualization, e.g. the mutual fund example, market segmentation) • Methods • from AI, statistics, machine learning, database, ….

  7. Overview • Components of DM environment • data (typically a DW) • model (what data, which factors to include, how to define good customerwhat are the relevant business questions…) • technique (algorithms, NN or DT, personalization techniques)

  8. Overview • New developments • Mining in new areas • Genomics • National security • Website data • multi-media • text • image

  9. Overview • Summary: DM = a high value-adding activity in processing data • Tie closely with CRM (e.g. B & L 2000, Wiley)

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