K-means clustering is one of the top 10 algorithms in the field data mining and knowledge discovery. The uniform effect in the k-means clustering reveals that, the imbalance nature of the data source hampered the performance in terms of efficient knowledge discovery. In this paper, we proposed a novel clustering algorithm known as Precise Reduction Sampling K-means (PRS_K-means) for efficient handling of imbalance data and reducing the uniform effect. The experiments shows that the algorithm can not only give attention to different instances of sub clusters for identify the intrinsic properties of the instances for clustering; and it performs better than K-means in terms of reduction in error rate and has higher accuracy and recall rate for improved performance.

Data Mining, Knowledge Discovery, Clustering, K-means, imbalance data, uniform effect, under sampling, PRS_K-means

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