Subspace Clustering of Very Sparse High-Dimensional Data

Peng, Hankui and Pavlidis, Nicos Georgios and Eckley, Idris Arthur and Tsalamanis, Ioannis (2019) Subspace Clustering of Very Sparse High-Dimensional Data. In: 2018 IEEE International Conference on Big Data (Big Data). IEEE, USA, pp. 3780-3783. ISBN 9781538650356

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Abstract

In this paper we consider the problem of clustering collections of very short texts using subspace clustering. This problem arises in many applications such as product categorisation, fraud detection, and sentiment analysis. The main challenge lies in the fact that the vectorial representation of short texts is both high-dimensional, due to the large number of unique terms in the corpus, and extremely sparse, as each text contains a very small number of words with no repetition. We propose a new, simple subspace clustering algorithm that relies on linear algebra to cluster such datasets. Experimental results on identifying product categories from product names obtained from the US Amazon website indicate that the algorithm can be competitive against state-of-the-art clustering algorithms.

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Contribution in Book/Report/Proceedings
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ID Code:
129248
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Deposited On:
30 Nov 2018 15:18
Refereed?:
Yes
Published?:
Published
Last Modified:
31 Mar 2020 01:04