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Granular-rule extraction to simplify data

Granulation simplifies the data to better understand its complexity. It comforts this understanding by extracting the structure of data, essentially in big data or cloud computing scales. It can extract a simple granular-rules set from a complex data set. Granulation is associated with theory of fuz...

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Main Authors: Mashinchi, R., Selamat, A., Ibrahim, S., Krejcar, O.
Format: Article
Published: Springer Verlag 2015
Subjects:
T58.5-58.64 Information technology
Online Access:http://eprints.utm.my/id/eprint/59292/
http://dx.doi.org/10.1007/978-3-319-15705-4_41
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spelling my.utm.592922021-09-12T01:33:09Z http://eprints.utm.my/id/eprint/59292/ Granular-rule extraction to simplify data Mashinchi, R. Selamat, A. Ibrahim, S. Krejcar, O. T58.5-58.64 Information technology Granulation simplifies the data to better understand its complexity. It comforts this understanding by extracting the structure of data, essentially in big data or cloud computing scales. It can extract a simple granular-rules set from a complex data set. Granulation is associated with theory of fuzzy information granulation, which can be supported by fuzzy C-mean clustering. However, intersections of fuzzy clusters create redundant granular-rules. This paper proposes a granular-rules extraction method to simplify a data set into a granular- rule set with unique granular-rules. It performs based on two stages to construct and prune the granular-rules. We use four data sets to reveal the results, i.e., wine, servo, iris, and concrete compressive strength. The results reveal the ability of proposed method to simplify data sets by 58% to 91%. Springer Verlag 2015 Article PeerReviewed Mashinchi, R. and Selamat, A. and Ibrahim, S. and Krejcar, O. (2015) Granular-rule extraction to simplify data. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9012 . pp. 421-429. ISSN 3029-743 http://dx.doi.org/10.1007/978-3-319-15705-4_41 DOI: 10.1007/978-3-319-15705-4_41
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic T58.5-58.64 Information technology
spellingShingle T58.5-58.64 Information technology
Mashinchi, R.
Selamat, A.
Ibrahim, S.
Krejcar, O.
Granular-rule extraction to simplify data
description Granulation simplifies the data to better understand its complexity. It comforts this understanding by extracting the structure of data, essentially in big data or cloud computing scales. It can extract a simple granular-rules set from a complex data set. Granulation is associated with theory of fuzzy information granulation, which can be supported by fuzzy C-mean clustering. However, intersections of fuzzy clusters create redundant granular-rules. This paper proposes a granular-rules extraction method to simplify a data set into a granular- rule set with unique granular-rules. It performs based on two stages to construct and prune the granular-rules. We use four data sets to reveal the results, i.e., wine, servo, iris, and concrete compressive strength. The results reveal the ability of proposed method to simplify data sets by 58% to 91%.
format Article
author Mashinchi, R.
Selamat, A.
Ibrahim, S.
Krejcar, O.
author_facet Mashinchi, R.
Selamat, A.
Ibrahim, S.
Krejcar, O.
author_sort Mashinchi, R.
title Granular-rule extraction to simplify data
title_short Granular-rule extraction to simplify data
title_full Granular-rule extraction to simplify data
title_fullStr Granular-rule extraction to simplify data
title_full_unstemmed Granular-rule extraction to simplify data
title_sort granular-rule extraction to simplify data
publisher Springer Verlag
publishDate 2015
url http://eprints.utm.my/id/eprint/59292/
http://dx.doi.org/10.1007/978-3-319-15705-4_41
_version_ 1712285053577330688
score 13.211869

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