Please use this identifier to cite or link to this item:
|Title||AVOIDING NOISE AND OUTLIERS IN K-MEANS.|
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.
|Published in||Computing & Information Systems|
|Series||Volume: 15, Number: 2|
|Item link||Item Link|
|Files in this item|
|There are no files associated with this item.|