An Improved Parameter less Data Clustering Technique based on Maximum Distance of Data and Lioyd k-means Algorithm
Copyright policy )An Improved Parameter less Data Clustering Technique based on Maximum Distance of Data and Lioyd k-means Algorithm
AbstractK-means algorithm is very well-known in large data sets of clustering. This algorithm is popular and more widely used for its easy implementation and fast working. However, it is well known that in the k-means algorithm, the user should specify the number of clusters in advance. In order to improve the performance of the K-means algorithm, various methods have been proposed. In this paper, has been presented an improved parameter less data clustering technique based on maximum distance of data and Lioyd k-means algorithm. The experimental results show that the use of new approach to defining the centroids, the number of iterations has been reduced where the improvement was 60%.
- Universiti Malaysia Terengganu Malaysia
- Universiti Malaysia Pahang Malaysia
Data Mining, K-Means Algorithm, Partitioning Clustering Algorithm, Clustering
Data Mining, K-Means Algorithm, Partitioning Clustering Algorithm, Clustering
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