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Multiple seeds based evolutionary algorithm for mining Boolean association rules
Citation
Kabir, MMJ and Xu, S and Kang, BH and Zhao, Z, Multiple seeds based evolutionary algorithm for mining Boolean association rules, Proceedings of the Trends and Applications in Knowledge Discovery and Data Mining Workshops (PAKDD 2016), 19 April 2016, Auckland, New Zealand, pp. 61-72. ISBN 9783319429953 (2016) [Refereed Conference Paper]
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Copyright Statement
Copyright 2016 Springer International Publishing
DOI: doi:10.1007/978-3-319-42996-0_6
Abstract
Most of the association rule mining algorithms use a single seed for
initializing a population without paying attention to the effectiveness of an initial
population in an evolutionary learning. Recently, researchers show that an initial
population has significant effects on producing good solutions over several
generations of a genetic algorithm. There are two significant challenges raised
by single seed based genetic algorithms for real world applications: (1) solutions
of a genetic algorithm are varied, since different seeds generate different initial
populations, (2) it is a hard process to define an effective seed for a specific
application. To avoid these problems, in this paper we propose a new multiple
seeds based genetic algorithm (MSGA) which generates multiple seeds from
different domains of a solution space to discover high quality rules from a large
data set. This approach introduces m-domain model and m-seeds selection
process through which the whole solution space is subdivided into m-number of
same size domains and from each domain it selects a seed. By using these seeds,
this method generates an effective initial population to perform an evolutionary
learning of the fitness value of each rule. As a result, this method obtains strong
searching efficiency at the beginning of the evolution and achieves fast convergence
along with the evolution. MSGA is tested with different mutation and
crossover operators for mining interesting Boolean association rules from different
real world data sets and compared the results with different single seeds
based genetic algorithms.
Item Details
| Item Type: | Refereed Conference Paper |
|---|---|
| Keywords: | multiple seeds, genetic algorithm, Boolean association rules |
| Research Division: | Information and Computing Sciences |
| Research Group: | Machine learning |
| Research Field: | Neural networks |
| Objective Division: | Information and Communication Services |
| Objective Group: | Information systems, technologies and services |
| Objective Field: | Information systems, technologies and services not elsewhere classified |
| UTAS Author: | Kabir, MMJ (Mr Mir Kabir) |
| UTAS Author: | Xu, S (Dr Shuxiang Xu) |
| UTAS Author: | Kang, BH (Professor Byeong Kang) |
| UTAS Author: | Zhao, Z (Dr Zongyuan Zhao) |
| ID Code: | 110175 |
| Year Published: | 2016 |
| Deposited By: | Information and Communication Technology |
| Deposited On: | 2016-07-18 |
| Last Modified: | 2018-02-28 |
| Downloads: | 0 |
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