Data Mining Techniques And Their Applications

2322 Words Oct 24th, 2014 10 Pages
Data Mining Techniques and Their Applications in Financial Data Analysis
Deepika Sattu, 800721246, dsattu@uncc.edu

Abstract— Data mining is a logical process that is used to search through large amount of data in order to find useful data [2].There are many different types of analysis that can be done in order to retrieve information from big data. Each type of analysis will have a different impact or result. Which type of data mining technique you should use really depends on the type of business problem that you are trying to solve.

Keywords: Clustering, Decision Trees, Classification, Prediction

I. INTRODUCTION

Data is very critical for any organization, industry or business process. Data which was in gigabytes or terabytes in the past has nowadays risen up to peta bytes, exa bytes. I.e. there has been an enormous amount of increase in the size of the data.

As the complexity involved in handling/modifying such kind of data is large, it is not at all possible to manually analyze or predict data in real time. The data in some clusters may be growing while in others it may be declining. Information generated is very useful for business decision making. Data mining consists of useful techniques such as Clustering and Association rules, these techniques can be used to predict the future trends based on the Item-sets. Clustering is used to group similar item-sets while association is used to get obtained from huge trading data using these rules. The data in…
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