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Published in:   Vol. 4 Issue 1 Date of Publication:   June 2015

Clustering Algorithms for Outlier Detection Performance Analysis

S. Vijayarani,S. Maria Sylviaa, A.Sakila

Page(s):   36-39 ISSN:   2278-2397
DOI:   10.20894/IJDMTA. Publisher:   Integrated Intelligent Research (IIR)

Data mining is the method of extracting the data from large database. Various data mining techniques are clustering, classification, association analysis, regression, summarization, time series analysis and sequence analysis, etc. Clustering is one of the important tasks in mining and is said to be unsupervised classification. Clustering is the techniques which is used to group similar objects or processes. In this work four clustering algorithms namely K-Means, Farthest first, EM, and Hierarchical are analyzedby the performance factors clustering accuracy, number of outliers detected and execution time. This performance analysis is carried out in BUPA (liver disorder) dataset. This work is performed in WEKA data mining tool.