Novel Clustering Approach that Employs Genetic Algorithm with New Representation Scheme and Multiple Objectives

Du, Jun; Korkmaz, Emin Erkan; Alhajj, Reda; Barker, Ken

doi:10.1007/978-3-540-30076-2_22

Cited by 16 publications

(24 citation statements)

References 8 publications

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“…As in MOCK, this approach also does not require the number of clusters to be known beforehand. In Korkmaz (2006), the author refines his approach in Du et al (2004) by clustering the data in two stages. In MOKGA (Ozyer et al 2004), a similar approach to Du et al (2004) is proposed.…”

Section: Related Workmentioning

confidence: 99%

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Multiobjective evolutionary clustering of Web user sessions: a case study in Web page recommendation

2009

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In this study, we experiment with several multiobjective evolutionary algorithms to determine a suitable approach for clustering Web user sessions, which consist of sequences of Web pages visited by the users. Our experimental results show that the multiobjective evolutionary algorithm-based approaches are successful for sequence clustering. We look at a commonly used cluster validity index to verify our findings. The results for this index indicate that the clustering solutions are of high quality. As a case study, the obtained clusters are then used in a Web recommender system for representing usage patterns. As a result of the experiments, we see that these approaches can successfully be applied for generating clustering solutions that lead to a high recommendation accuracy in the recommender model we used in this paper.

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Section: Related Workmentioning

confidence: 99%

“…Another approach proposed in Du et al (2004) is based on NPGA (Niched Pareto Genetic Algorithm) (Horn et al 1994) and it uses the same representation as in MOCK. The two complementary objectives are ''minimizing the total within cluster variation'' and ''minimizing the number of clusters''.…”

Section: Related Workmentioning

confidence: 99%

Multiobjective evolutionary clustering of Web user sessions: a case study in Web page recommendation

2009

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“…Also, if the traditional genetic operators are used with those representations, they could significantly damage the good solutions that are being developed during the search. For example, (Du et al, 2004) showed that the standard one-point crossover has various disadvantages and is not suitable for grouping problems.…”

Section: Grouping Representationsmentioning

confidence: 99%

“…This representation has been applied in many grouping problems (Handl and Knowles, 2007), but it still suffers the same issues of redundancy and lack of appropriate operators as the former representations. In (Du et al, 2004), a restricted version of the LBA known as the Linear Linkage Encoding (LLE) has been proposed, in which backward links are not allowed and, apart from an ending node, each node has only one node pointing to it. LLE successfully eliminates the redundancy problem that debilitates former representations, and has been applied to a variety of grouping problems such as Bin Packing, Graph Colouring and Timetabling (Ülker et al, 2006;Ülker et al, 2008).…”

Section: Grouping Representationsmentioning

confidence: 99%

A grouping hyper-heuristic framework: Application on graph colouring

Elhag¹,

Özcan²

2015

Expert Systems with Applications

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Grouping problems are hard to solve combinatorial optimisation problems which require partitioning of objects into a minimum number of subsets while a given objective is simultaneously optimized. Selection hyper-heuristics are high level general purpose search methodologies that operate on a space formed by a set of low level heuristics rather than solutions. Most of the recently proposed selection hyper-heuristics are iterative and make use of two key methods which are employed successively; heuristic selection and move acceptance. At each step, a new solution is produced after a selected heuristic is applied to the solution in hand and then the move acceptance method is used to decide whether the resultant solution replaces the current one or not. In this study, we present a selection hyper-heuristic framework including a fixed set of low level heuristics specifically designed for grouping problems. The performance of different hyperheuristics using different components within the framework is investigated on a representative grouping problem of graph colouring. Additionally, the hyper-heuristic performing the best on graph colouring is applied to a benchmark of examination timetabling instances. The empirical results shows that the proposed grouping hyper-heuristic is not only sufficiently general, but also able to achieve high quality solutions for graph colouring and examination timetabling.

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“…In this study, we used a powerfull grouping representation referred to as linear linkage encoding (LLE) [17], which is a restricted form of the more general linkage encoding scheme. Each gene in the LLE representation represents one object, and stores an integer value (index) which represents a link between that object and another object belonging to the same group.…”

Section: Grouping Representationmentioning

confidence: 99%

A grouping hyper-heuristic framework based on linear linkage encoding for graph coloring

Elhag¹,

Özcan²

2013

2013 13th UK Workshop on Computational Intelligence (UKCI)

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Abstract-Grouping problems are a class of combinatorial optimization problems in which the task is to search for the best partition of a set of objects into a collection of mutually disjoint subsets while satisfying a given set of constraints. Typical examples include data clustering, graph coloring and exam timetabling problems. Selection hyper-heuristics based on iterative search frameworks are high level general problem solving methodologies which operate on a set of low level heuristics to improve an initially generated solution via heuristic selection and move acceptance. In this paper, we describe a selection hyper-heuristic framework based on an efficient representation referred to as linear linkage encoding for multi-objective grouping problems. This framework provides the implementation of a fixed set of low level heuristics that can work on all grouping problems where a trade-off between a given objective and number of groups is sought. The empirical results on graph coloring problem indicates that the proposed grouping hyper-heuristic framework can indeed provide high quality solutions.

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Novel Clustering Approach that Employs Genetic Algorithm with New Representation Scheme and Multiple Objectives

Cited by 16 publications

References 8 publications

Multiobjective evolutionary clustering of Web user sessions: a case study in Web page recommendation

Multiobjective evolutionary clustering of Web user sessions: a case study in Web page recommendation

A grouping hyper-heuristic framework: Application on graph colouring

A grouping hyper-heuristic framework based on linear linkage encoding for graph coloring

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