Data Mining


Data Mining is the process of extracting knowledge hidden from large volumes of raw data.The knowledge must be new, not obvious, and one must be able to use it. 

Knowledge discovery differs from traditional information retrieval from databases.In traditional DBMS, database records are returned in response to a query; while in knowledge discovery, what is retrieved is not explicit in the database. Rather, it is implicit patterns. The Process of discovering such patterns is termed data mining. 

Data mining finds these patterns and relationships using data analysis tools and techniques to build models. There are two main kinds of models in data mining. One is predictive models, which use data with known results to develop a model that can be used to explicitly predict values. Aother is descriptive models, which describe patterns in existing data. All the models are abstract representations of reality, and can be guides to understanding business and suggest actions.

Following questions are probably be answered if information hidden among megabytes of data in your database can be found and utilized. Modeling the investigated system, discovering relations that connect variables in a database are the subject of data mining.
  • What goods should be promoted to this customer?
  • What is the probability that a certain customer will respond to a planned promotion?
  • Can one predict the most profitable securities to buy/sell during the next trading session?
  • Will this customer default on a loan or pay back on schedule?
  • What medical diagnose should be assigned to this patient?
  • How large the peak loads of a telephone or energy network are going to be?
  • Why the facility suddenly starts to produce defective goods?

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