Clustering of text documents with keyword weighting function

A. Christy, G. Meera Gandhi and S. Vaithyasubramanian

International Journal of Intelligent Enterprise, 2019, vol. 6, issue 1, 19-31

Abstract: In this digital world, data is available in abundance everywhere and it is growing at a phenomenal rate. Making data available readily for decision making is an important task of data analyst. In this article, we propose an unsupervised learning algorithm for text document clustering by adopting keyword weighting function. Documents are pre-processed and relevant keywords based on their weights are grouped together. Clustered keyword weighting (CKW) takes each class in the training collection as a known cluster, and searches for feature weights iteratively to optimise the clustering objective function, in order to retrieve the best clustering result. Performance of CKW is validated by clustering BBC news collection text collections. Experiments were conducted with simple K-means, hierarchical clustering algorithms and our keyword weighting and clustering approach has shown improved cluster quality compared to the other methods.

Keywords: documents; cluster; unsupervised; feature; K-means; normalised. (search for similar items in EconPapers)
Date: 2019
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