Abstract An online local adaptive fuzzy C-means (OLAFCM) algorithm for high dimensional data is proposed based on fuzzy C-means (FCM) and local adaptive clustering (LAC). Through assigning corresponding weights to its attributes,OLAFCM can make each cluster distribute in a subspace spanned by the combination of different attributes. Thus,the proposed algorithm not only avoids the risk of loss of information encountered in global dimensionality reduction techniques,but also is suitable for clustering data streams. Compared to state-of-the-art partition-based online clustering algorithms using global dimensionality reduction methods,the proposed algorithm has better performance on artificial and real datasets.
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