Efficient Document Clustering System Based on Probability Distribution of K-Means (PD K-Means) Model
Journal Title: International Journal of the Computer, the Internet and Management - Year 2018, Vol 26, Issue 1
Abstract
In document clustering system, some documents with the same similarity scores may fall into different clusters instead of same cluster due to calculate similarity distance between pairs of documents based on geometric measurements. To tackle this point, probability distribution of K-Means (PD K-Means) algorithm is proposed. In this system, documents are clustered based on proposed probability distribution equation instead of similarity measure between objects. It can also solve initial centroids problems of K-Means by using Systematic Selection of Initial Centroid (SSIC) approach. So, it not only can generate compact and stable results but also eliminates initial cluster problem of K-Means. According to the experiment, F-measure values increase about 0.28 in 20 NewsGroup dataset, 0.26 in R8 and 0.14 in R52 from Reuter21578 datasets. The evaluations demonstrate that the proposed solution outperforms than original method and can be applied for various standard and unsupervised datasets.
Authors and Affiliations
Tin Thu Zar Win, Nang Aye Aye Htwe, Moe Moe Aye
Keywords
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- EP ID EP598664
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