The Impacts of Singular Value Decomposition Algorithm Toward Indonesian Language Text Documents Clustering View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2019

AUTHORS

Muhammad Ihsan Jambak , Fathey Mohammed , Novita Hidayati , Rusdi Efendi , Rifkie Primartha

ABSTRACT

Data with a high dimension mean that the data has many sets of variables. Conventional clustering algorithms are only able to deal with low-dimensional data conditions, so the clustering of high-dimensional data objects poses a challenge for resolving the solution. Indonesian language documents have specificity due to grammatical differences compared to English documents. Therefore, this research implements a Singular Value Decomposition (SVD) algorithm to reduce these dimensions and analyses its impact on the accuracy of clustering methods such as k-Means and k-Medoids. Results show that combining SVD with both clustering methods increased accuracy by 11 and 10%, respectively. Additionally, processing times were also proven to be faster. More... »

PAGES

173-183

Book

TITLE

Recent Trends in Data Science and Soft Computing

ISBN

978-3-319-99006-4
978-3-319-99007-1

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-99007-1_17

DOI

http://dx.doi.org/10.1007/978-3-319-99007-1_17

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1106894390


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