An Extraction Of Knowledge Using Meta Heuristic Models

9008 Words Jul 30th, 2015 37 Pages
Soft Computing
An Extraction of Knowledge using meta heuristic models --Manuscript Draft--
Manuscript Number:
SOCO-D-15-00222
Full Title:
An Extraction of Knowledge using meta heuristic models
Article Type:
Original Research
Keywords:
Data mining, association rule, fuzzy logic, neural network, particle swarm optimization, artificial bee colony algorithm and harmony search algorithm
Abstract:
Huge amounts of data are collected nowadays from different application domains and are not feasible to analyze all these data manually. Knowledge Discovery in Databases (KDD) is the nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. In recent years, soft computing became more and more attractive for the researchers, who work in the related research field of data mining. This paper concerns primarily about how to use soft computing model to extract knowledge from data mining (database). The data mining preprocessing techniques have been applied on the available data to clean it in proper form to extract the knowledge from the data. Thereafter, statistical analysis and soft computing techniques have been applied on the clean data to select the preferable model. The decision of the preferable model has to be achieved based on the maximum number of minimum value of the parameters of residual analysis and average error. The preferable model has been used to extract the knowledge from the data mining. The goal of the…
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