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For document data which are typically represented as high dimensional and sparse vectors, cosine distance based Hyperspherical Fuzzy C-Means(HFCM) has been shown to be more effective than classic Euclidean distance based fuzzy c-means(FCM) for document categorization. The existing HFCM approach assumes a static dataset and performs clustering in a batch mode. This design makes HFCM no more suitable...
As an important technique of data analysis, clustering plays an important role in finding the underlying pattern structure embedded in unlabeled data. Clustering algorithms that need to store all the data into the memory for analysis become infeasible when the dataset is too large to be stored. To handle such large data, incremental clustering approaches are proposed. The key idea behind these approaches...
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