DATA MINING BASICS

Data mining means extracting or ‘mining’ knowledge from raw data. It is a process of analyzing data from different perspectives and summarizing it into useful information to increase revenue of an enterprise etc.

Joseph P. Bigus in his book “DATA MINING AND NEURAL NETWORKS ” define data mining as ‘data mining is the efficient discovery of valuable, non obvious information from a large collection of data’. 

Data mining is also referred to as knowledge discovery process.

Steps in knowledge discovery process are following:-

1. Determination of business objectives.

2. Selection and preparation of data.

3. Application of suitable data mining techniques.

4. Evaluation and application of results.

Also, some major data mining techniques are:- 

A) CLUSTER DETECTION:

In this technique the cluster detection algorithm searches for groups or clusters of data elements that are similar to one and another.

B) DECISION TREES:

This technique applies to classification and prediction. Decision trees represent rules. We can use these rules to retrieve records falling into a certain category. It is a series of questions. Each question determines what follow up question is best to be asked next.

C) LINK ANALYSIS:

This algorithm is extremely useful for finding patterns from relationships. Thus technique mines relationships and discover knowledge.

D) ASSOCIATION DISCOVERY:

Associations discovery algorithms find combinations where the presence of one item suggests the presence of another.

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