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Data Mining And Evolutionary Algorithms For Multi Objective Optimization Problems

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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…show more content…
It also helps to present the data with reduced set of samples without representing the whole data set, which reduces the complexity in space and reduction in time. Data mining interesting knowledge includes identifying the relations, differences; groups based one the similar features extracted. Data mining mainly includes the mechanism for representing the data, Specification on required information and method to search the algorithm. Representation model used to represent the underlying data and interpretability of model which interacts with human.
Data mining prediction model works on the process of identifying the patterns based on the historical information to predict the new incoming data sets. This prediction modelling is much useful in the case of decision making process in the business models. On the other way, Descriptive model describes the data in an efficient way by means of grouping the data by using clustering; association rules principles of data mining.
Evolutionary Algorithms are working mainly based on two features variation (recombination and mutation) to create the necessary diversity and selection of attributes to force to push quality. Variation operator can be a recombination in the case of binary operation and mutation in the case of unary, The evolutionary algorithms works on the basic steps start with initializing, Initialize the set with the random set of candidate, Evaluate the
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