TITLE

AVOIDING NOISE AND OUTLIERS IN K-MEANS

AUTHOR(S)
Jnena, Rami; Timraz, Mohammed; Ashour, Wesam
PUB. DATE
October 2011
SOURCE
Computing & Information Systems;Oct2011, Vol. 15 Issue 2, p1
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Applying k-means algorithm on the datasets that include large number of noise and outlier objects, gives unclear clusters results. In this paper we proposed a new technique for avoiding these noise and outliers by applying some preprocessing and post processing steps for the dataset that have to be clustered by k-means. Our experimental results demonstrated that our scheme can avoid and eliminate the noise and outliers of the dataset in an efficient and accurate way.
ACCESSION #
67435671

 

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