Research Article Open Access

Differentiate Clustering Approaches for Outlier Detection

Abstract

Data mining is a process of extracting hidden and useful information from the data and the knowledge discovered by data mining is previously unknown, potentially useful, and valid and of high quality. There are several techniques exist for data extraction. Clustering is one of the techniques amongst them. In clustering technique, we form the group of similar objects (similarity in terms of distance or there may be any other factor). Outlier detection as a branch 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 detect outlier but Clustering is one of the efficient techniques. In this paper, I have compared the result of different Clustering techniques in terms of time complexity and proposed a new solution by adding fuzziness to already existing Clustering techniques.

Ms. Neeraj Bansal, Mr.Amit Chugh

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