The Korea Institute of Information and Commucation Engineering 2012; 10(1): 40-44
Published online March 31, 2012
https://doi.org/10.6109/jicce.2012.10.1.040
© Korea Institute of Information and Communication Engineering
Gene Algorithm of Crowd System of Data Mining
Data mining, which is attracting public attention, is a process of drawing out knowledge from a large mass of data. The key technique in data mining is the ability to maximize the similarity in a group and minimize the similarity between groups. Since grouping in data mining deals with a large mass of data, it lessens the amount of time spent with the source data, and grouping techniques that shrink the quantity of the data form to which the algorithm is subjected are actively used. The current grouping algorithm is highly sensitive to static and reacts to local minima. The number of groups has to be stated depending on the initialization value. In this paper we propose a gene algorithm that automatically decides on the number of grouping algorithms. We will try to find the optimal group of the fittest function, and finally apply it to a data mining problem that deals with a large mass of data.
Keywords Algorithm,Crowd,Data mining,Gene
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The Korea Institute of Information and Commucation Engineering 2012; 10(1): 40-44
Published online March 31, 2012 https://doi.org/10.6109/jicce.2012.10.1.040
Copyright © Korea Institute of Information and Communication Engineering.
Gene Algorithm of Crowd System of Data Mining
Park, Jong-Min;
Department of Cyber Security, Chosun College of Science & Technology
Abstract
Data mining, which is attracting public attention, is a process of drawing out knowledge from a large mass of data. The key technique in data mining is the ability to maximize the similarity in a group and minimize the similarity between groups. Since grouping in data mining deals with a large mass of data, it lessens the amount of time spent with the source data, and grouping techniques that shrink the quantity of the data form to which the algorithm is subjected are actively used. The current grouping algorithm is highly sensitive to static and reacts to local minima. The number of groups has to be stated depending on the initialization value. In this paper we propose a gene algorithm that automatically decides on the number of grouping algorithms. We will try to find the optimal group of the fittest function, and finally apply it to a data mining problem that deals with a large mass of data.
Keywords: Algorithm,Crowd,Data mining,Gene
