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A parameter-free hybrid clustering algorithm used for malware categorization
ASID'09 Proceedings of the 3rd international conference on Anti-Counterfeiting, security, and identification in communication
Performance management of IT services delivery
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Stock price movement prediction using representative prototypes of financial reports
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Hierarchical K-means clustering algorithm based on silhouette and entropy
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
Development and performance evaluation of neural network classifiers for Indian internet shoppers
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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Hierarchical and k-means clustering are two major analytical tools for unsupervised microarray datasets. However, both have their innate disadvantages. Hierarchical clustering cannot represent distinct clusters with similar expression patterns. Also, as clusters grow in size, the actual expression patterns become less relevant. K-means clustering requires a specified number of clusters in advance and chooses initial centroids randomly; in addition, it is sensitive to outliers. We present a novel hybrid approach to combined merits of the two and discard disadvantages we mentioned above. It is different from existed method: carry out hierarchical clustering first to decide location and number of clusters in the first round and run the K-means clustering in another round. The brief idea is we cluster around half data through hierarchical clustering and succeed by K-means for the rest half in one single round. Also, our approach provides a mechanism to handle outliers. Comparing with existed hybrid clustering approach and K-means clustering in 2 different distance measure on Eisen驴s yeast microarray data, our method always generate much higher quality clusters.