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中文摘要：
基于模糊C均值（FCM）和局部自适应聚类（LAC）提出一种针对高维数据的联机局部自适应模糊c均值聚类算法（OLAFCM）．OLAFCM通过为各类属性分别赋以相应的局部权重，使各类属性分布在不同属性组合的张量子空间内，从而有效降低采用全局降维方法造成的信息损失，同时适合聚类数据流．最后，在人工模拟和真实数据集上验证OLAFCM比之现有基于全局降维的划分联机聚类算法具有更好的性能．
英文摘要：
An online local adaptive fuzzy C-means （OLAFCM） algorithm for high dimensional data is proposed based on fuzzy C2means （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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