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A new multi-objective cluster ensemble based on modularity maximization

JOURNAL OF ENGINEERING RESEARCH 2016年第2期4卷 1-16页

作者： Khodaparasti, Mohammad Ganji, Mohadeseh Amirgholipour, Saeed Sharifi, Aboosaleh Mohammad Islamic Azad Univ Dept Comp Ramsar Branch Ramsar Iran Univ Melbourne Dept Comp & Informat Syst Melbourne Vic Australia Islamic Azad Univ Young Researchers & Elite Club Ramsar Branch Ramsar Iran

Conventional clustering algorithms utilize only one single criterion that may not conform to diverse shapes of the underlying clusters. But in this paper, we use two important criteria and propose a new multi-objective cluster ensemble model to empower finding clusters of different types. The first criterion is the well-known sum of squared error. The second criterion is modularity, which is originally a measure of evaluating communities in social networks. We maximize modularity as a consensus function of cluster ensemble. In order to add further improvement, we also modify the Non-dominant sorting genetic algorithm (NSGAII) and propose a specialized crossover operator for it. Experimental results over thirteen UCI real data sets show that the proposed method outperforms other clustering methods.

关键词： Cluster ensemble genetic algorithm modularity multi-objective clustering non-dominant sorting

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Parallel alternatives for evolutionary multi-objective optimization in unsupervised feature selection

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第9期42卷 4239-4252页

作者： Kimovski, Dragi Ortega, Julio Ortiz, Andres Banos, Raul Univ Informat Sci & Technol Ohrid North Macedonia Univ Granada CITIC Dept Comp Architecture & Technol E-18071 Granada Spain Univ Malaga Dept Commun Engn E-29071 Malaga Spain Catholic Univ Murcia Dept Business Adm & Management Murcia Spain

Many machine learning and pattern recognition applications require reducing dimensionality to improve learning accuracy while irrelevant inputs are removed. This way, feature selection has become an important issue on these researching areas. Nevertheless, as in past years the number of patterns and, more specifically, the number of features to be selected have grown very fast, parallel processing constitutes an important tool to reach efficient approaches that make possible to tackle complex problems within reasonable computing times. In this paper we propose parallel multi-objective optimization approaches to cope with high-dimensional feature selection problems. Several parallel multi-objective evolutionary alternatives are proposed, and experimentally evaluated by using some synthetic and BCI (Brain Computer Interface) benchmarks. The experimental results show that the cooperation of parallel evolving subpopulations provides improvements in the solution quality and computing time speedups depending on the parallel alternative and data profile. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Feature selection High-dimensional data multi-objective clustering Parallel evolutionary algorithms Speedup models Unsupervised classification

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An ensemble game theoretic approach for multi-objective optimization

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AI COMMUNICATIONS 2015年第3期28卷 553-566页

作者： Badami, Mahsa Mozafari, Niloofar Hamzeh, Ali Hashemi, Sattar Shiraz Univ Dept Comp Sci Engn & IT Sch Elect & Comp Engn Shiraz Iran

Recently multi-objective clustering has been extensively explored due to its appearance of new applications in many domains. However, in many applications, there is more than a single objective which is needed to be optimized in the context of the application, such as facility location, ad hoc networks and sensor networks. These domains must optimize two objectives of compactness and equi-partitioning which may be conflicted in some situations. Existing algorithms have high complexity. In this paper, we propose an Ensemble Game Theoretic approach for multi-objective clustering method which optimizes two objectives of compactness and equi-partitioning, simultaneously. We compare our algorithm on variety of data sets including synthetic and real ones. The remarkable results are very promising and demonstrate the efficiency of presented approach both in performance and complexity.

关键词： multi-objective clustering game theory ensemble clustering equi-partitioning compactness

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A survey on nature inspired metaheuristic algorithms for partitional clustering

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SWARM AND EVOLUTIONARY COMPUTATION 2014年 16卷 1-18页

作者： Nanda, Satyasai Jagannath Panda, Ganapati Malaviya Natl Inst Technol Jaipur Dept Elect & Commun Engn Jaipur 302017 Rajasthan India Indian Inst Technol Sch Elect Sci Bhubaneswar 751013 Odisha India

The partitional clustering concept started with K-means algorithm which was published in 1957. Since then many classical partitional clustering algorithms have been reported based on gradient descent approach. The 1990 kick started a new era in cluster analysis with the application of nature inspired metaheuristics. After initial formulation nearly two decades have passed and researchers have developed numerous new algorithms in this field. This paper embodies an up-to-date review of all major nature inspired metaheuristic algorithms employed till date for partitional clustering. Further, key issues involved during formulation of various metaheuristics as a clustering problem and major application areas are discussed. (C) 2014 Published by Elsevier B.V.

关键词： Partitional clustering Nature inspired metaheuristics Evolutionary algorithms Swarm intelligence multi-objective clustering

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Feature selection in high-dimensional EEG data by parallel multi-objective optimization 16

Feature selection in high-dimensional EEG data by parallel m...

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16th IEEE International Conference on Cluster Computing (CLUSTER)

作者： Kimovski, Dragi Ortega, Julio Ortiz, Andres Banos, Raul Univ Informat Sci & Technol Ohrid North Macedonia Univ Granada CITIC Dept Comp Architecture & Technol Granada Spain Univ Malaga Dept Ingn Comunicac Malaga Spain Univ Catolica Murcia Dept Adm & Direcc Empresas Murcia Spain

ISBN: (纸本)9781479955480

Feature selection is required in many applications that involve high dimensional model building or classification problems. Many bioinformatics applications belong to this type. Recently, some approaches for supervised and unsupervised feature selection as a multi-objective optimization problem have been proposed. As the performance of unsupervised classification is evaluated through the quality of the obtained groups or clusters in the data set to be classified, it is difficult to define a suitable objective function that drives the selection of the features. Thus, several evaluation measures, and thus multiobjective clustering characterization, could provide a suitable set of features for unsupervised classification. In this paper, we consider the parallel implementation of a multi-objective feature selection that makes it possible to apply it to complex classification problems such as those having many features to select, and specifically high-dimensional data sets with much more features than data items. In this paper, we propose masterworker implementations of two different parallel evolutionary models, the parallel computation of the cost functions for the individuals in the population, and the parallel execution of evolutionary multi-objective procedures on subpopulations. The experiments accomplished on different benchmarks, including some related with feature selection in classification of EEG (Electroencephalogram) signals for BCI (Brain Computer Interface) applications, show the benefits of parallel processing not only for decreasing the running time, but also for improving the solution quality.

关键词： Feature selection multi-objective clustering Parallel multiobjective optimization Wrapper methods

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A Framework for multi-objective clustering and Its Application to Co-location Mining

A Framework for Multi-Objective Clustering and Its Applicati...

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5th International Conference on Advanced Data Mining and Applications

作者： Jiamthapthaksin, Rachsuda Eick, Christoph F. Vilalta, Ricardo Univ Houston Dept Comp Sci Houston TX 77204 USA

ISBN: (纸本)9783642033476

The goal of multi-objective clustering (MOC) is to decompose a dataset into similar groups maximizing multiple objectives in parallel. In this paper. we provide a methodology, architecture and algorithms that, based on a large set of objectives, derive interesting clusters regarding two or more of those objectives. The proposed architecture relies on clustering algorithms that support plug-in fitness functions and on multi-run clustering in which clustering, algorithms are run multiple times maximizing different subsets of objectives that are captured in compound fitness functions. MOC provides search engine type capabilities to users, enabling them to query a large set of clusters with respect to different objectives and thresholds. We evaluate the proposed MOC framework in a case study that centers on spatial co-location mining. the goal is to identify regions in which high levels of Arsenic concentrations are co-located with high concentrations of other chemicals in the Texas water supply.

关键词： multi-objective clustering knowledge discovery spatial data mining co-location mining clustering with plug-in fitness functions

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On Evolving Neighborhood Parameters for Fuzzy Density clustering

On Evolving Neighborhood Parameters for Fuzzy Density Cluste...

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IEEE Congress on Evolutionary Computation

作者： Banerjee, Amit Penn State Univ Harrisburg Sch Sci Engn & Technol Middletown PA 17057 USA

ISBN: (纸本)9781479904549;9781479904532

The problem of identifying core patterns with the correct neighborhood parameters is a major challenge for density-based clustering techniques derived from the popular DBSCAN algorithm. An evolutionary approach to optimizing the assignment of core patterns is proposed in this paper. Key ideas presented here include a genetic representation that associates distinct neighborhood parameters with potential core patterns and specialized crossover and mutation operators. The evolutionary framework is based on the multi-objective NSGA-II algorithm, with simplified fitness measures derived from local (neighborhood) information. clustering experiments on both synthetic and benchmark clustering datasets are presented and results are compared to the original DBSCAN, fuzzy DBSCAN and k-means.

关键词： fuzzy density clustering evolutionary clustering multi-objective clustering DBSCAN neighborhood parameters

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multi-objective design of hierarchical consensus functions for clustering ensembles via genetic programming

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DECISION SUPPORT SYSTEMS 2011年第4期51卷 794-809页

作者： Coelho, Andre L. V. Fernandes, Everlandio Faceli, Katti Univ Fortaleza Ctr Technol Sci Grad Program Appl Informat BR-60811905 Fortaleza CE Brazil Univ Fed Sao Carlos BR-18052780 Sorocaba SP Brazil

This paper investigates a genetic programming (GP) approach aimed at the multi-objective design of hierarchical consensus functions for clustering ensembles. By this means, data partitions obtained via different clustering techniques can be continuously refined (via selection and merging) by a population of fusion hierarchies having complementary validation indices as objective functions. To assess the potential of the novel framework in terms of efficiency and effectiveness, a series of systematic experiments, involving eleven variants of the proposed GP-based algorithm and a comparison with basic as well as advanced clustering methods (of which some are clustering ensembles and/or multi-objective in nature), have been conducted on a number of artificial, benchmark and bioinformatics datasets. Overall, the results corroborate the perspective that having fusion hierarchies operating on well-chosen subsets of data partitions is a fine strategy that may yield significant gains in terms of clustering robustness. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Cluster analysis clustering ensembles multi-objective clustering Hierarchical fusion Partition selection Genetic programming

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Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation

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APPLIED INTELLIGENCE 2011年第1期35卷 110-122页

作者： Ozyer, Tansel Zhang, Ming Alhajj, Reda Univ Calgary Dept Comp Sci Calgary AB T2N 1N4 Canada TOBB ETU Econ & Technol Univ Dept Comp Engn TR-06560 Ankara Turkey Global Univ Dept Comp Sci Beirut Lebanon

Skyline computation in databases has been a hot topic in the literature because of its interesting applications. The basic idea is to find non-dominated values within a database. The task is mainly a multi-objective optimization process as described in this paper. This motivated for our approach that employs a multi-objective genetic algorithm based clustering approach to find the pareto-optimal front which allows us to locate skylines within a given data. To tackle large data, we simply split the data into manageable subsets and concentrate our analysis on the subsets instead of the whole data at once. The proposed approach produced interesting results as demonstrated by the outcome from the conducted experiments.

关键词： Skyline computation multi-objective clustering Genetic algorithm Cluster validation

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Automatic clustering using MOCLONAL for Classifying Actions of 3D Human Models

Automatic Clustering using MOCLONAL for Classifying Actions ...

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IEEE Symposium on Humanities, Science and Engineering Research

作者： S. J. Nanda G. Panda School of Electrical Sciences Indian Institute of Technology Bhubaneswar Odisha-751013 India

ISBN: (纸本)9781467313117

Conventional clustering algorithms use a single objective function optimization criterion for classification which may not provide satisfactory results to determine the underlying clusters in many datasets. In such scenario multi-objective algorithms are preferred which improve the clustering performance due to additional knowledge of data properties in the form of objective functions. In this paper we have proposed an automatic multi-objective clustering algorithm based on clonal selection principle of artificial immune system (AIS) and is termed as MOCLONAL. The proposed algorithm is capable of providing a single best solution from the Pareto optimal archive which mostly satisfy the user requirement. Simulation studies on synthetic and real life datasets demonstrate the superior performance of the proposed algorithm compared to benchmark multi-objective clustering algorithm MOCK. An interesting application of the proposed algorithm have been demonstrated to classify the normal and aggressive actions of 3D human models.

关键词： multi-objective clustering Automatic clustering MOCLONAL MOCK Human Model

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