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  1. Yangon Technological University
  2. Department of Computer Engineering and Information Technology

Efficient Document Clustering System Basedon Probability Distribution ofK-Means(PD K-Means) Model

http://hdl.handle.net/20.500.12678/0000003101
http://hdl.handle.net/20.500.12678/0000003101
46fab796-ce26-4e83-b144-2b31387e2a2b
3e307aa6-01b1-4570-90a6-506632d707ab
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Efficient Efficient Document Clustering System Basedon Probability Distribution ofK-Means(PD K-Means) Model Tin Thu Zar Win (Ph D IJSHRE journal).pdf (152 Kb)
Publication type
Journal article
Upload type
Publication
Title
Title Efficient Document Clustering System Basedon Probability Distribution ofK-Means(PD K-Means) Model
Language en
Publication date 2017-10-06
Authors
Tin Thu Zar Win
Nang Aye Aye Htwe
Moe Moe Aye
Description
<p>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.&nbsp; To tackle this point, probability distribution of KMeans (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 20NewsGroup 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.</p>
Keywords
Initial Centroid, Probability Distribution, PD K-Means, SSIC
Identifier 10.5281/zenodo.3131916
Journal articles
Issue 8
International Journal of Software & Hardware Research in Engineering
pp. 1-6
Volume 5
Conference papers
Books/reports/chapters
Thesis/dissertations
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