A Revised and efficient K-means Clustering Algorithm
DOI:
https://doi.org/10.26438/ijcse/v6i12.118124Keywords:
Clustering, CentroidsAbstract
In digital era large volumes of data are generated by enterprises. Mining on this large volume of data provides valuable insights into user behaviors and helps to improve the business. Various Machine learning algorithms are proposed for data mining. Clustering is an important data mining algorithm for grouping the records and analyzing the data. K-means is a most used Clustering algorithm, but the time taken to cluster large volume of records is high. To reduce the clustering time many approaches are proposed in literature. In this work an improved K-means clustering is proposed which is able to reduce the clustering time.
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