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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  4/2015 - 3

Automatic Mining of Numerical Classification Rules with Parliamentary Optimization Algorithm

KIZILOLUK, S. See more information about KIZILOLUK, S. on SCOPUS See more information about KIZILOLUK, S. on IEEExplore See more information about KIZILOLUK, S. on Web of Science, ALATAS, B. See more information about ALATAS, B. on SCOPUS See more information about ALATAS, B. on SCOPUS See more information about ALATAS, B. on Web of Science
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,850 KB) | Citation | Downloads: 479 | Views: 1,746

Author keywords
classification algorithms, computational intelligence, data mining, heuristic algorithms, optimization

References keywords
optimization(15), algorithm(7), science(5), parliamentary(5), mining(5), classification(5), rules(4), global(4), alatas(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2015-11-30
Volume 15, Issue 4, Year 2015, On page(s): 17 - 24
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.04003
Web of Science Accession Number: 000368499800003
SCOPUS ID: 84949980538

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In recent years, classification rules mining has been one of the most important data mining tasks. In this study, one of the newest social-based metaheuristic methods, Parliamentary Optimization Algorithm (POA), is firstly used for automatically mining of comprehensible and accurate classification rules within datasets which have numerical attributes. Four different numerical datasets have been selected from UCI data warehouse and classification rules of high quality have been obtained. Furthermore, the results obtained from designed POA have been compared with the results obtained from four different popular classification rules mining algorithms used in WEKA. Although POA is very new and no applications in complex data mining problems have been performed, the results seem promising. The used objective function is very flexible and many different objectives can easily be added to. The intervals of the numerical attributes in the rules have been automatically found without any a priori process, as done in other classification rules mining algorithms, which causes the modification of datasets.

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Cited-By ISI Web of Science

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SCOPUS® Times Cited: 3
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Cited-By CrossRef

[1] Web Pages Classification with Parliamentary Optimization Algorithm, Kiziloluk, Soner, Ozer, Ahmet Bedri, International Journal of Software Engineering and Knowledge Engineering, ISSN 0218-1940, Issue 03, Volume 27, 2017.
Digital Object Identifier: 10.1142/S0218194017500188

[2] SM-RuleMiner: Spider monkey based rule miner using novel fitness function for diabetes classification, Cheruku, Ramalingaswamy, Edla, Damodar Reddy, Kuppili, Venkatanareshbabu, Computers in Biology and Medicine, ISSN 0010-4825, Issue , 2017.
Digital Object Identifier: 10.1016/j.compbiomed.2016.12.009

[3] ANT_FDCSM: A novel fuzzy rule miner derived from ant colony meta-heuristic for diagnosis of diabetic patients, Anuradha, , Singh, Akansha, Gupta, Gaurav, Journal of Intelligent & Fuzzy Systems, ISSN 1064-1246, Issue 1, Volume 36, 2019.
Digital Object Identifier: 10.3233/JIFS-172240

Updated 3 days, 3 hours ago

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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania

All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

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