A Multiobjective Evolution Algorithm Based Rule Certainty Updating Strategy in Big Data Environment

With the ubiquitous deployment of the mobile devices and the explosive growth of Internet traffic, an emerging method called association rules mining (ARM) is proposed to solve the problem of mining potential value of existing big data. However, massive ARM methods focus on positive rules which are...

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Vydané v:	GLOBECOM : proceedings : 2017 IEEE Global Communications Conference : Singapore, 4-8 December 2017 s. 1 - 6
Hlavní autori:	Jun Mi, Kun Wang, Bo Liu, Fei Ding, Yanfei Sun, Huawei Huang
Médium:	Konferenčný príspevok..
Jazyk:	English
Vydavateľské údaje:	IEEE 01.12.2017
Predmet:	Automobiles Big Data Complexity theory Data mining Evolutionary computation Optimization Upper bound
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Popis
Shrnutí:	With the ubiquitous deployment of the mobile devices and the explosive growth of Internet traffic, an emerging method called association rules mining (ARM) is proposed to solve the problem of mining potential value of existing big data. However, massive ARM methods focus on positive rules which are easy to ignore interesting information because of negative ones. This paper studies a practical problem of combing negative rules in ARM research. Specifically, we propose a rule certainty updating strategy (RCUS) to combine positive rules with negative rules, which consists of two parts: initialization and updating. To solve the large scale problem with negative rules, the proposed strategy decomposes the large scale problem into several relatively small ones by an improved multiobjective evolutionary algorithm (MOEA) with gene representation and certainty. Simulation results show that our method is outstanding when the scale of attributes and examples is increasing.
DOI:	10.1109/GLOCOM.2017.8255002