Data Mining Techniques And Their Applications

1891 Words Nov 16th, 2014 8 Pages
Data Mining Techniques and Their

Applications
Deepika Sattu, 800721246, dsattu@uncc.edu

Abstract— Data mining is logical process that is used to extract or “mining” large amount of data in order to find useful data [2]. Knowledge discovery from Data or KDD is synonym for Data Mining[13].There are many different types of techniques that can be used to retrieve information from large amount of data. Each type of technique will generate different results. The type of data mining technique that should be selected depends on the type of business problem that we are trying to solve.
Keywords: Clustering, Decision Trees, Classification,

Prediction

I. INTRODUCTION

Data is very critical for any organization. In an organization every
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Each type of analysis will generate different results. Which type of data mining technique we should use really depends on the type of business problem that we are trying to solve [1].

Steps that are required to make certain decisions about business development are mentioned below: [2]

1. Exploration:
In Exploration, step data is cleaned and transformed into another form of data. Data cleaning is used to remove noise and to correct inconsistence in the data. Based on the problem in business, variables and nature of data are determined

2. Pattern Identification: Next step after Exploration is pattern Identification. There are many different patterns based on business problem, pattern need to be identified.

3. Deployment: After pattern Identification the next step is to deploy patterns to achieve output.[2]

There are several crucial data mining techniques
That is used in many data mining projects now days. Some of the data mining techniques are described briefly in flowing sections.

A. CLASSIFICATION:

Classification is a classic data mining technique which is based on machine learning. In this technique each item in a set of data are classified in predefined classes or groups.
This technique is represented in various forms such as Classification Rules (IF-THEN), Decision trees, mathematical formulas or neural networks. [13] To use classification we will develop
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