文章基本信息

标题：Outlier Detection Using Hybrid Genetic Algorithm and Bacterial Foraging Optimization Algorithm
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作者：Dr. Amit Verma ; Er. Parminder Kaur ; Sharnjeet Kaur 等
期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2015
卷号：6
期号：3
页码：41-43
语种：English
出版社：Ayushmaan Technologies
摘要：Data Mining is the process of extract useful information from the large data by using different mining techniques. Clustering and classification manage the large amount of data into different clusters according to their properties. Sometimes data arranged in clusters contain outliers that degrade the performance of the system. The outliers detected by K-Mean genetic algorithm also contain information; to detect this information and outlier’s properly K-Mean genetic bacterial foraging algorithm is applied. This paper also presents the comparison between K-Mean genetic algorithm and K-Mean genetic bacterial foraging algorithm.
关键词：K-mean;Genetic algorithm;Outlier Detection;Recall;False acceptance rate;Accuracy;False rejection rate.