CHAP. 1 - INTRODUCTION TO DM

CHAP. 1 - INTRODUCTION TO DM

1. Data mining is a process or a set of activities of extracting interesting, non-trivial, implicit, previously unknown and potentially useful information from a large amount of data to discover meaningful pattern or knowledge.

2. Purpose

   • Find relationship
   • Makes prediction

3. Knowledge discovery in database

   • A comprehensive process of using data mining methods to find useful information and patterns in data
   • Step of KDD

4. Data mining task

   • Classification
     • The process of finding a model for class attribute as a function of the values of other attributes
   • Clustering
     • The process of find cluster from a given set of data points, each having set of attribute and a similarity measure among them
   • Association rule
     • Produce dependency rules which will predict the occurrence of an item from a given set of records which contain some number of item.
   • Deviation analysis
     • Discovering the most significant changes in data from previously measured or normative values

5. Techniques

   • Decision tree
   • Association rule
   • k-means
   • Neural network
   • Naive bayes
   • k-nearest neighbour
   • Statistical method

6. Application

   • Fraud detection
     • Goal : Predict fraudulent cases in credit card transaction
   • Direct marketing
     • Goal : Reduce cost of mailing by targetting a set of customers likely to buy a new product

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