Abstract
Document clusters are the way to segment a certain set of text into racial groups. Nowadays all records are in electronic form due to the problem of retrieving appropriate document from the big database. The objective is to convert text consisting of daily language into a structured database format. Different documents are thus summarized and presented in a uniform manner. Big quantity, high dimensionality and complicated semantics are the difficult issue of document clustering. The aim of this article is primarily to cluster multi-sense word embedding using three distinct algorithms (K-means, DBSCAN, CURE) using singular value decomposition. In this performance measures are measured using different metrics.
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