Mining Clustering Dimensions

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Mining Clustering Dimensions

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by Sajib Dasgupta , Vincent Ng

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11 - 2 self

BibTeX

@MISC{Dasgupta_miningclustering,
    author = {Sajib Dasgupta and Vincent Ng},
    title = {Mining Clustering Dimensions},
    year = {}
}

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Abstract

Many real-world datasets can be clustered alongmultiple dimensions. Forexample, text documentscanbeclusterednotonlybytopic, but also by the author’s gender or sentiment. Unfortunately, traditional clustering algorithms produce only a single clustering of a dataset, effectively providing a user with just a single view of the data. In this paper, we propose a new clustering algorithm that can discover in an unsupervised manner each clustering dimension along which a dataset can be meaningfully clustered. Its ability to revealthe important clustering dimensions of a dataset in an unsupervised manner is particularly appealing for those users who have no idea of how a dataset can possibly be clustered. Wedemonstrateitsviabilityonseveral challenging text classification tasks. 1.

Keyphrases

clustering dimension unsupervised manner single view single clustering alongmultiple dimension many real-world datasets traditional clustering algorithm text classification task new clustering algorithm text documentscanbeclusterednotonlybytopic author gender

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