期刊名称：International Journal of Innovative Research in Computer and Communication Engineering
印刷版ISSN：2320-9798
电子版ISSN：2320-9801
出版年度：2013
卷号：1
期号：2
出版社：S&S Publications
摘要：Data mining is a process of extracting hidden and useful information from the data and the knowledgediscovered by data mining is previously unknown, potentially useful, and valid and of high quality. There are severaltechniques exist for data extraction. Clustering is one of the techniques amongst them. In clustering technique, we formthe group of similar objects (similarity in terms of distance or there may be any other factor). Outlier detection as abranch of data mining has many important applications and deserves more attention from data mining community.Therefore, it is important to detect outlier from the extracted data. There are so many techniques existing to detectoutlier but Clustering is one of the efficient techniques. In this paper, I have compared the result of different Clusteringtechniques in terms of time complexity and proposed a new solution by adding fuzziness to already existing Clusteringtechniques.
关键词：Clustering; Data Mining; Outlier Detection; Data Mining
