Data Mining And Evolutionary Algorithms For Multi Objective Optimization Problems

1427 Words Mar 4th, 2015 6 Pages
Data mining & Evolutionary algorithms for Multi-objective Optimization problems: A study.

Data mining is the process of extracting the knowledge from the huge database available. The ultimate aim of data mining involves prediction based on the knowledge gained. Data mining is known as Knowledge Discovery in Databases (KDD) which is different ways mainly prediction and description. When data mining applied over the real time problem which puts us into trouble by having conflicting objectives to achieve which involves various measures which needs to minimized or maximized without affecting. This various constraints given the way to lead the concept of using the evolutionary algorithms. In the multi-objective optimization problems whose aim is to provide a set of non-dominated solutions which improves objective without affecting others objective function. The EA and data mining can be combined for the efficient identification of the feature identification, classification, clustering and association rule mining etc. In this paper we just made a study on the various classical problem solving using the evolutionary algorithms and various methodologies for adaptive population models and sizing methodologies.

Keywords: Data mining, KDD, Evolutionary algorithms, Multi-objective optimization.

I. INTRODUCTION
Data mining is the process of identifying, interesting and useful knowledge from the huge data sets. The objective of data mining can be either descriptive or predictive…
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