Title: Subspace Clustering of Very Sparse High-Dimensional Data,
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.