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Data Mining: Introduction

Lecture Notes for Chapter 1 Introduction to Data Mining by Tan, Steinbach, Kumar

© Tan,Steinbach, Kumar

Introduction to Data Mining

4/18/2004

1

Why Mine Data? Commercial Viewpoint
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)
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 2

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
4,000,000 3,500,000 3,000,000 2,500,000 2,000,000 1,500,000 1,000,000 500,000 0 1995 1996 1997

The Data Gap
Total new disk (TB) since 1995

Number of analysts
1998 1999
4

© Tan,Steinbach, KumarKamath, V. Kumar, “Data Mining for Mining and Engineering Applications” From: R. Grossman, C. Introduction to Data Scientific 4/18/2004

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

© Tan,Steinbach, Kumar

Introduction to Data

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