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Research on Evolutionary Mining of Classification Ruleset
Author: WangZhiChun
Tutor: LiMinQiang
School: Tianjin University
Course: Information Management and Information Systems
Keywords: Data Mining Classification rules Evolutionary algorithm Coevolution
CLC: TP311.13
Type: Master's thesis
Year: 2010
Downloads: 76
Quote: 0
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Abstract
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Data mining from large databases to automatically extract novel , useful , understandable process mode . Classification rules is most commonly used as a form of knowledge representation in data mining . Classification rule mining aims to find a set of \Numerous studies show that the rule-based classification system has very good results in the handling of classified database and sparse high -dimensional data . Evolutionary algorithm - based rules mining depth research work done mainly include the following aspects: Recalling the commonly used classification methods in data mining , summarize and analyze the rule-based classification methods , evolutionary algorithms do systems research , in-depth analysis of the existing rule mining methods based on evolutionary algorithms , and proposed research direction and focus of the article . Multigroup coevolution build multiple rule populations evolved simultaneously . Each population evolutionary purpose of mining rules a part of the training data can be correctly classified . To evaluate the individuals in a population , the selection rules on behalf of the rules in order to form a complete collection from other populations . The algorithm to guide the different parts of different individuals in the population coverage of the training data , the composition of the better the full rule sets . Dynamically adjust the data coverage of the population and various groups in the evolutionary process . More generally , this new algorithm can produce more accurate set of rules . Propose a new coevolutionary genetic algorithm to search multiple rules at the same time in the same population . Algorithm according to the rules in each population coverage of training data fitness value assigned to the individual , so that the rules of the fitness evaluation is no longer independent of the other rules in the population , in order to achieve the coevolution between individual . This algorithm makes the rules set the correct rate and efficiency of mining have been improved .
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