Subspace Clustering of Very Sparse High-Dimensional Data
This addresses the problem of clustering sparse high-dimensional data for applications like product categorization, fraud detection, and sentiment analysis, though it appears incremental.
The paper tackles clustering of very short texts by proposing a new subspace clustering algorithm based on linear algebra, which achieves competitive performance against state-of-the-art methods in product categorization tasks on Amazon data.
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.