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Big Data Summarization Using Novel Clustering Algorithm and Semantic Feature Approach

Big Data Summarization Using Novel Clustering Algorithm and Semantic Feature Approach

Shilpa G. Kolte, Jagdish W. Bakal
Copyright: © 2017 |Volume: 4 |Issue: 3 |Pages: 10
ISSN: 2334-4598|EISSN: 2334-4601|EISBN13: 9781522515739|DOI: 10.4018/IJRSDA.2017070108
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MLA

Kolte, Shilpa G., and Jagdish W. Bakal. "Big Data Summarization Using Novel Clustering Algorithm and Semantic Feature Approach." IJRSDA vol.4, no.3 2017: pp.108-117. http://doi.org/10.4018/IJRSDA.2017070108

APA

Kolte, S. G. & Bakal, J. W. (2017). Big Data Summarization Using Novel Clustering Algorithm and Semantic Feature Approach. International Journal of Rough Sets and Data Analysis (IJRSDA), 4(3), 108-117. http://doi.org/10.4018/IJRSDA.2017070108

Chicago

Kolte, Shilpa G., and Jagdish W. Bakal. "Big Data Summarization Using Novel Clustering Algorithm and Semantic Feature Approach," International Journal of Rough Sets and Data Analysis (IJRSDA) 4, no.3: 108-117. http://doi.org/10.4018/IJRSDA.2017070108

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Abstract

This paper proposes a big data (i.e., documents, texts) summarization method using proposed clustering and semantic features. This paper proposes a novel clustering algorithm which is used for big data summarization. The proposed system works in four phases and provides a modular implementation of multiple documents summarization. The experimental results using Iris dataset show that the proposed clustering algorithm performs better than K-means and K-medodis algorithm. The performance of big data (i.e., documents, texts) summarization is evaluated using Australian legal cases from the Federal Court of Australia (FCA) database. The experimental results demonstrate that the proposed method can summarize big data document superior as compared with existing systems.

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