An Efficient Partitional Clustering Algorithm Based on Splitting & Merging Strategy
The A-means has been one of the most accepted partitioning clustering algorithms among a wide range of clustering algorithms, due to its superior scalability and efficiency. But, key drawbacks of Ameans algorithm is that it usually creates empty clusters depending on initial cluster centers and number of clusters dependency. In order to tackle these challenges, we have proposed an efficient partitional clustering algorithm utilizing A-means clustering. The concept proposed in the work can generate the initial clusters to initialize A-means clustering. The initial cluster centers of the -means clustering are identified by applying the splitting and merging strategy, which employs density and mean parameter of the initial partitions. The experimentation is carried out on wine datasets and the experimental results ensured that the proposed algorithm has achieved better clustering accuracy and less computation time compared with the k-means clustering algorithm.
Data Mining Clustering, Partitional clustering kmeans Initial Cluster Centers Empty Clusters, Density
Parvinder S. Sandhu Dalvinder S. Dhaliwal S. N. Panda
Professor & Chair (Deptt. Of CSE) Rayat & Bahra Institute of Engg. & Bio-Tech., Mohali, India Assistant Prof. (Deptt. Of CSE) RIMIT Institute of Engg. & Tech. Mandi Gobindgarh, Punjab, India Director & Professor, Regional Institute of Mgmt. & Tech.,Mandi Gobindgarh, India
国际会议
成都
英文
421-424
2011-01-14（万方平台首次上网日期，不代表论文的发表时间）