Time-sensitive clustering evolving textual data streams
by Mohamed Ammar; Adel Hidri; Minyar Sassi Hidri
International Journal of Computer Applications in Technology (IJCAT), Vol. 63, No. 1/2, 2020

Abstract: Clustering a stream of text documents is an emerging subject of interest since it is widely used in analysing the content in social media and e-journals. The aim is to find a certain structure for unlabelled data based on a similarity criterion. However, few works have focused on this field and fall in this perspective, that's why a new document clustering approach adapted to a stream of text data and test it on news articles data sets is proposed. A distributed representation of words is used, and a bottom-up approach is used to represent documents as vectors on a unit hyper-sphere. The proposed approach gains its roots from the SPherical k-means (SPKM) algorithm and its underlying mixture of von-Mises Fisher (vMF) distributions. The proposed approach yields comparable results to baseline batch algorithm for stable data streams and superior results for rapidly evolving data streams.

Online publication date: Tue, 30-Jun-2020

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