Data Mining - Definition, Stages, Advantages and Drawbacks

Running head: DATA MINING
Data mining
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1Data mining
Table of Contents
Introduction:....................................................................................................................................2
Discussion:.......................................................................................................................................2
Conclusion:......................................................................................................................................4
References........................................................................................................................................5

2Data mining
Introduction:
Data mining also known as Knowledge Discovery in Databases (KDD), is defined as the
process of drawing or extracting inherent, earlier unrecognized and conceivably useful
information and knowledge from a large data collection or data sets in databases or data
warehouse. The extraction process is done by using automated data analysis techniques, which
sorts data sets to identify patterns to establish relationships. These are used by the data mining
toll to predict future trends. The necessary difference between traditional data analysis such as
query, online application of analysis and reporting with data mining is that data mining excavate
information and reveal knowledge on ground of indistinct presumption (Sahu, Shrma &
Gondhalakar, 2011).
Discussion:
Data mining stages: They are as follows.
1) Preparation of data (or pre-processing of data): Data preparation is data manipulation into
suitable form for further processing and analysis. The process of data preparation consists of
various tasks such as collecting, integrating, structuring, translating and validating data and those
tasks cannot be fully automated and many of them are tedious and time-consuming. The main
reason for data preparation is to ensure that the required prepared information for analysis must
be consistent and accurate. Data preparation is required for successful data mining (Provost &
Fawcett, 2013).
2) Data Mining: This stage is the nucleus of the overall process, which primarily utilises the
gathered techniques of mining as well as tools for dealing with the data. This stage involves the
following activities.
Data mining method collection