Data Mining and its uses

Data mining is the process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis. Data mining is defined as a process used to extract usable data from a larger set of any raw data. In Business it is useful for discovering patterns and relationships in data to help make better decisions. That’s why it is otherwise known as Knowledge discovery. It is the computer-assisted process of digging through and analyzing enormous sets of data and then extracting the meaning of the data. Data mining tools predict behaviors and future trends, allowing businesses to make proactive, knowledge-driven decisions.

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In data mining, association rules are created by analyzing data for frequent if/then patterns, then using the support and confidence criteria to locate the most important relationships within the data. Support is how frequently the items appear in the database, while confidence is the number of times if/then statements are accurate.

The following are the various types of data which can be mined:

  • Flat Files.
  • Relational Databases.
  • Data Ware house.
  • Transactional Databases.
  • Multimedia Databases.
  • Spatial Databases.
  • Time Series Databases.
  • World Wide Web(WWW)


There are four stages of Data Mining. First we can collect data from data sources. These range from databases to news wires & are considered a problem definition. Secondly we can exploration or gathering the sampling and transformation of data. Thirdly Modeling means users create a model, test it & then evaluate. Fourthly Deploying Models means take an action based on the results from the models.

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