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Unit 2: Data Mining Concept

For many years, statistics have been used to analyze data in an effort to find correlations, patterns, notes
and dependencies. However, with an increased in technology more and more data are available,
which greatly exceed the human capacity to manually analyze them. Before the 1990’s, data
collected by bankers, credit card companies, department stores and so on have little used. But
in recent years, as computational power increases, the idea of data mining has emerged. Data
mining is a term used to describe the “process of discovering patterns and trends in large data
sets in order to find useful decision-making information.” With data mining, the information
obtained from the bankers, credit card companies, and department stores can be put to good
use
figure 2.1: Data Mining chart

2.4 architecture of Data Mining

Based on the above definition, the architecture of a typical data mining system may have the
following major components (Figure 2.2):

1. Information repository: This is one or a set of databases, data warehouses, spreadsheets, or
other kinds of information repositories. Data cleaning and data integration techniques may
be performed on the data.

2. Database or data warehouse server: The database or data warehouse server is responsible
for fetching the relevant data, based on the user’s data mining request.
3. Knowledge base: This is the domain knowledge that is used to guide the search or evaluate
the interestingness of resulting patterns.
4. Data mining engine: This is essential to the data mining system and ideally consists of a set of
functional modules for tasks such as characterisation, association and correlation analysis,
classification, prediction, cluster analysis, outlier analysis, and evolution analysis.
5. Pattern evaluation module: This component typically employs interestingness measures
and interacts with the data mining modules so as to focus the search toward interesting
patterns.

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