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Clustering streaming data presents the problem of not having all the data available at one time. Further, the total size of the data may be larger than will fit in the available memory of a typical computer. If the data is very large, it is a challenge to apply fuzzy clustering algorithms to get a partition in a timely manner. In this paper, we present an online fuzzy clustering algorithm which can...
Clustering algorithms for streaming data sets are gaining importance due to the availability of large data streams from different sources. Recently a number of streaming algorithms have been proposed using crisp algorithms such as hard c means or its variants. The crisp cases may not be easily generalized to fuzzy cases as these two groups of algorithms try to optimize different objective functions...
There are an increasing number of large labeled and unlabeled data sets available. Clustering algorithms are the best suited for helping one make sense out of unlabeled data. However, scaling iterative clustering algorithms to large amounts of data has been a challenge. The computation time can be very great and for data sets that will not fit in even the largest memory, only carefully chosen subsets...
Recently several algorithms for clustering large data sets or streaming data sets have been proposed. Most of them address the crisp case of clustering, which cannot be easily generalized to the fuzzy case. In this paper, we propose a simple single pass (through the data) fuzzy c means algorithm that neither uses any complicated data structure nor any complicated data compression techniques, yet produces...
Combining multiple clustering solutions is important for obtaining a robust clustering solution, merging distributed clustering solutions, and scaling to large data sets. The combination of multiple clustering solutions within a scalable and robust framework for large data sets is discussed. A scalable framework requires both cluster ensemble creation and merging to be efficient in terms of time and...
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