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- W4302062192 abstract "In recent years, data streaming has gained prominence due to advances in technologies that enable many applications to generate continuous flows of data. This increases the need to develop algorithms that are able to efficiently process data streams. Additionally, real-time requirements and evolving nature of data streams make stream mining problems, including clustering, challenging research problems. In this paper, we propose a one-pass streaming soft clustering (membership of a point in a cluster is described by a distribution) algorithm which approximates the soft version of the k-means objective function. Soft clustering has applications in various aspects of databases and machine learning including density estimation and learning mixture models. We first achieve a simple pseudo-approximation in terms of the hard k-means algorithm, where the algorithm is allowed to output more than $k$ centers. We convert this batch algorithm to a streaming one (using an extension of the k-means++ algorithm recently proposed) in the cash register model. We also extend this algorithm when the clustering is done over a moving window in the data stream." @default.
- W4302062192 created "2022-10-06" @default.
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- W4302062192 date "2012-07-26" @default.
- W4302062192 modified "2023-09-28" @default.
- W4302062192 title "Achieving Approximate Soft Clustering in Data Streams" @default.
- W4302062192 doi "https://doi.org/10.48550/arxiv.1207.6199" @default.
- W4302062192 hasPublicationYear "2012" @default.
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