Algorithms for clustering data
Algorithms for clustering data
ACM Computing Surveys (CSUR)
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Unsupervised Learning with Mixed Numeric and Nominal Data
IEEE Transactions on Knowledge and Data Engineering
An Optimized Approach on Applying Genetic Algorithm to Adaptive Cluster Validity Index
IIH-MSP '07 Proceedings of the Third International Conference on International Information Hiding and Multimedia Signal Processing (IIH-MSP 2007) - Volume 02
Genetic Algorithm with Histogram Construction Technique
ICETET '09 Proceedings of the 2009 Second International Conference on Emerging Trends in Engineering & Technology
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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Clustering is the most important task in unsupervised learning and clustering validity is a major issue in cluster analysis. In this paper, a new strategy called Clustering Algorithm Based on Histogram Threshold (HTCA) is proposed to improve the execution time. The HTCA method combines a hierarchical clustering method and Otsu's method. Compared with traditional clustering algorithm, our proposed method would save at leastten several times of execution time without losing the accuracy. From the experiments, we find that the performance with regard to speed up the execution time of the HTCA is much better than traditional methods.