Data Mining Methods and Algorithms

Data mining is a part of a bigger framework, referred to as knowledge discovery in databases (KDD) that covers a complex process from data preparation to knowledge modelling. Main data mining task is classification which has main work to assign each record of a database to one of the predefined classes. The next is clustering which works in the way that it finds groups of records instead of only one record that are close to each other according to metrics defined by user. The next task is association which defines implication rules on the basis of that subset of record attributes can be defined. Data mining is the main important step to reach the knowledge discovery. An algorithm in data mining (or machine learning) is a set of heuristics and calculations that creates a model from data. To create a model, the algorithm first analyses the data that has been provided by the user, followed by detailed scanning to mark any repetitive pattern in their nature. The results of this analysis are then processed through an algorithm over multiple iterations to find the optimum parameters for creating the mining model. 

  • Graph data mining
  • genome mining
  • automated data acquisition
  • visual data-mining
  • Text Mining

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