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WIREs Data Mining Knowl Discov
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Clustering of nonstationary data streams: A survey of fuzzy partitional methods

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Data streams have arisen as a relevant research topic during the past decade. They are real‐time, incremental in nature, temporally ordered, massive, contain outliers, and the objects in a data stream may evolve over time (concept drift). Clustering is often one of the earliest and most important steps in the streaming data analysis workflow. A comprehensive literature is available about stream data clustering; however, less attention is devoted to the fuzzy clustering approach, even though the nonstationary nature of many data streams makes it especially appealing. This survey discusses relevant data stream clustering algorithms focusing mainly on fuzzy methods, including their treatment of outliers and concept drift and shift. This article is categorized under Technologies > Machine Learning Technologies > Computational Intelligence Fundamental Concepts of Data and Knowledge > Data Concepts
Partitioning methods
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Quality indices of the fuzzy clustering methods on the soil moisture and temperature dataset: (a) PC, (b) PE, (c) XB
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Quality indices of the fuzzy clustering methods on the Smtp (KDDCUP99) dataset: (a) PC, (b) PE, (c) XB
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Quality indices of the fuzzy clustering methods on the Gaussian dataset: (a) PC, (b) PE, (c) XB
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Fuzzy clustering methods of data streams
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Technologies > Machine Learning
Technologies > Computational Intelligence
Fundamental Concepts of Data and Knowledge > Data Concepts

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