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- W1972652010 abstract "K-means clustering is one of the most commonly used methods for classification and data-mining. When the amount of data to be clustered is huge, and/or when data becomes available in increments, one has to devise incremental K-means procedures. Current research on incremental clustering does not address several of the specific problems of incremental K-means including the seeding problem, sensitivity of the algorithm to the order of the data, and the number of clusters. In this paper we present static and dynamic single-pass incremental K-means procedures that overcome these limitations." @default.
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- W1972652010 date "2014-03-01" @default.
- W1972652010 modified "2023-09-25" @default.
- W1972652010 title "Dynamic Incremental K-means Clustering" @default.
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- W1972652010 doi "https://doi.org/10.1109/csci.2014.60" @default.
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