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multi-objective clustering: a kernel based approach using Differential Evolution

CONNECTION SCIENCE 2019年第3期31卷 294-321页

作者： Nayak, Subrat Kumar Rout, Pravat Kumar Jagadev, Alok Kumar Siksha O Anusandhan Univ Dept Comp Sci & Engn Bhubaneswar 751030 Odisha India Siksha O Anusandhan Univ Dept Elect & Elect Engn Bhubaneswar Odisha India KIIT Univ Sch Comp Engn Bhubaneswar India

A multi-objective algorithm is always favoured over a single objective algorithm as it considers different aspects of a dataset in the form of various objectives. In this article, a multi-objective clustering algorithm has been proposed based on Differential Evolution. Here, three objectives have been considered to handle different complex datasets. In addition to this, a kernel function is hybridised with the objectives to evaluate the data on a hyperspace for reducing the impact of nonlinearity on cluster formation. Moreover, to get the best compromised solution from the Pareto front an effective fuzzy concept has been followed. Five metaheuristic approaches have been taken into consideration for performance comparison. These methodologies have been applied to twelve datasets and the result reveals the efficacy of the proposed model in data clustering.

关键词： multi-objective clustering Differential Evolution kernel clustering Pareto front

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multi-objective clustering WITH DATA- AND HUMAN-DRIVEN METRICS

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JOURNAL OF COMPUTER INFORMATION SYSTEMS 2011年第4期51卷 64-73页

作者： Kim, Yong Seog Utah State Univ Logan UT 84322 USA

While the usefulness of clustering analysis for knowledge discovery and information management is well accepted, it is difficult to objectively estimate the quality of clusters due to the fact that resulting clusters are heavily dependent on which and how many variables are used for clustering. Further, there are a number of heuristic criteria that can be used to estimate the quality of clusters and none of them completely dominates the others. To overcome these shortcomings, this study takes a multi-objective genetic algorithm (MOGA) approach to explore possible combinations of input variables to form clusters and evaluate cluster quality in a multi-objective space. In particular, each candidate cluster in MOGA is evolved and evaluated by both data-driven and human-driven metrics developed for this study. Note that human-driven metrics are introduced to measure managerial quality of clusters for efficient information management and effective decision making. The proposed system also allows the decision maker to navigate non-dominated cluster solutions and to choose one of them as the final solution. Experimental results on both synthetic and real data show promise in finding non-dominated cluster solutions with significant managerial insights for decision making.

关键词： multi-objective clustering cluster interestingness cluster relevance visualization Pareto optimization

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multi-objective clustering: A Data-Driven Analysis of MOCLE, MOCK and Δ-MOCK 28th

Multi-objective Clustering: A Data-Driven Analysis of MOCLE,...

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28th International Conference on Neural Information Processing

作者： Kultzak, Adriano Morimoto, Cristina Y. Pozo, Aurora de Souto, Marcilio C. P. Univ Fed Parana Dept Informat Curitiba Parana Brazil Univ Orleans LIFO Orleans France

ISBN: (纸本)9783030923068;9783030923075

We present a data-driven analysis of MOCK, Delta-MOCK, and MOCLE. These are three closely related approaches that use multi-objective optimization for crisp clustering. More specifically, based on a collection of 12 datasets presenting different proprieties, we investigate the performance of MOCLE and MOCK compared to the recently proposed Delta-MOCK. Besides performing a quantitative analysis identifying which method presents a good/poor performance with respect to another, we also conduct a more detailed analysis on why such a behavior happened. Indeed, the results of our analysis provide useful insights into the strengths and weaknesses of the methods investigated.

关键词： clustering methods multi-objective clustering multi-objective optimization Data mining

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Automatic multi-objective clustering based on game theory

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EXPERT SYSTEMS WITH APPLICATIONS 2017年第0期67卷 32-48页

作者： Heloulou, Imen Radjef, Mohammed Said Kechadi, Mohand Tahar Univ Abderrahmane Mira Dept Operat Res LaMOS Res Unit Rd Targua Ouzemour Bejaia 06000 Algeria Univ Coll Dublin Sch Comp Sci & Informat Dublin 4 Ireland

Data clustering is a very well studied problem in machine learning, data mining, and related disciplines. Most of the existing clustering methods have focused on optimizing a single clustering objective. Often, several recent disciplines such as robot team deployment, ad hoc networks, multi-agent systems, facility location, etc., need to consider multiple criteria, often conflicting, during clustering. Motivated by this, in this paper, we propose a sequential game theoretic approach for multi-objective clustering, called ClusSMOG-II. It is specially designed to optimize simultaneously intrinsically conflicting objectives, which are inter-cluster/intra-cluster inertia and connectivity. This technique has an advantage of keeping the number of clusters dynamic. The approach consists of three main steps. The first step sets initial clusters with their representatives, whereas the second step calculates the correct number of clusters by resolving a sequence of multi-objective multi-act sequential two-player games for conflict-clusters. Finally, the third step constructs homogenous clusters by resolving sequential two-player game between each cluster representative and the representative of its nearest neighbor. For each game, we define payoff functions that correspond to the model objectives. We use a methodology based on backward induction to calculate a pure Nash equilibrium for each game. Experimental results confirm the effectiveness of the proposed approach over state-of-the-art clustering algorithms. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： multi-objective clustering Sequential game Backward induction Nash equilibrium

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An enriched game-theoretic framework for multi-objective clustering

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APPLIED SOFT COMPUTING 2013年第4期13卷 1853-1868页

作者： Badami, Mahsa Hamzeh, Ali Hashemi, Sattar Shiraz Univ Sch Elect & Comp Engn Shiraz Iran

The framework of multi-objective clustering can serve as a competent technique in nowadays human issues ranging from decision making process to machine learning and pattern recognition problems. multi-objective clustering basically aims at placing similar objects into the same groups based on some conflicting objectives, which substantially supports the use of game theory to come to a resolution. Based on these understandings, this paper suggests Enriched Game Theory K-means, called EGTKMeans, as a novel multi-objective clustering technique based on the notion of game theory. EGTKMeans is specially designed to optimize two intrinsically conflicting objectives, named, compaction and equi-partitioning. The key contributions of the proposed approach are three folds. First, it formulates an elegant and novel payoff definition which considers both objectives with equal priority. The presented payoff function incorporates a desirable fairness into the final clustering results. Second, EGTKMeans performs better off by utilizing the advantages of mixed strategies as well as those of pure ones, considering the existence of mixed Nash Equilibrium in every game. The last but not the least is that EGTKMeans approaches the optimal solution in a very promising manner by optimizing both objectives simultaneously. The experimental results suggest that the proposed approach significantly outperforms other rival methods across real world and synthetic data sets with reasonable time complexity. Crown Copyright (C) 2012 Published by Elsevier B.V. All rights reserved.

关键词： multi-objective clustering Game theory Equi-partitioning Compaction

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A multi-act sequential game-based multi-objective clustering approach for categorical data

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NEUROCOMPUTING 2017年 267卷 320-332页

作者： Heloulou, Imen Radjef, Mohammed Said Kechadi, Mohand Tahar Univ Abderrahmane Mira Dept Operat Res LaMOS Res Unit Rd Targua Ouzemour Bejaia 06000 Algeria UCD Sch Comp Sci & Informat Dublin 4 Ireland

clustering categorical data, where no natural ordering can be found among the attributes values, has started drawing interest recently. Few clustering methods have been proposed to satisfy the categorical data requirements. Most of these methods have focused on optimizing a single measure, however, several applications in different areas need to consider multiple incommensurable criteria, often conflicting, during clustering. Motivated by this, we developed a multi-objective clustering approach for categorical data based on sequential games. It can automatically generate the correct number of clusters. The approach consists of three main phases. The first phase identifies initial clusters according to an initialization mechanism which has an important effect in the final clustering result. The second phase uses multi-act multi-objective sequential two-player games in order to determine the appropriate number of clusters. A methodology based on backward induction is used to calculate a pure Nash equilibrium for each game. Finally, the third phase constructs homogenous clusters by optimizing intra-cluster inertia. The performance of this algorithm has been studied on both simulated and real-world datasets. Comparisons with other clustering algorithms illustrate the effectiveness of the proposed approach. (C) 2017 Elsevier B.V. All rights reserved.

关键词： multi-objective clustering Categorical data multi-act sequential game Nash equilibrium

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Reference vector-based multi-objective clustering for high-dimensional data

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APPLIED SOFT COMPUTING 2019年 78卷 614-629页

作者： Liu, Chao Li, Yuanrui Zhao, Qi Liu, Chenqi Beijing Univ Technol Coll Econ & Management Beijing 100124 Peoples R China Modern Mfg Ind Dev Res Base Beijing Beijing 100124 Peoples R China Dickinson Coll Dept Math & Comp Sci Carlisle PA 17013 USA

Subspace clustering algorithms have shown their advantage in handling high-dimensional data by optimizing a linear combination of clustering criteria. However, setting the coefficients of these criteria items without prior knowledge will lead to inaccurate and poor robust clustering results. To address this problem, in this paper, we propose to optimize the multiple clustering criteria simultaneously without any predefined coefficients by a multi-objective evolutionary algorithm. Furthermore, to accelerate the convergence of the algorithm, we provide a novel local search method. In it, the multi-objective clustering problem is decomposed into many localized scalarizing sub-problems by reference vectors. Solutions are then locally searched around their associated sub-problems. Thirdly, we develop a knee-pruning fuzzy ensemble method for selecting the final solution. This method applies clustering ensemble in solutions selected from knee regions to get robust results. Experiments on UCI benchmarks and gene expression datasets show that our proposed algorithm can efficiently handle high-dimensional clustering problems without any user-defined coefficients. (C) 2019 Elsevier B.V. All rights reserved.

关键词： High-dimensional data multi-objective clustering Soft subspace clustering ESSC

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INVESTIGATION OF COMBINATIONAL clustering INDICES IN ARTIFICIAL IMMUNE multi-objective clustering

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COMPUTATIONAL INTELLIGENCE 2014年第1期30卷 115-144页

作者： Yang, Dongdong Jiao, Licheng Niu, Ruican Gong, Maoguo Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ China Xian 710071 Peoples R China Shaanxi Huanghe Grp Co LTD Xian Peoples R China

clustering validity indices play a core role in the unsupervised pattern classification. To date, some indices have been proposed and their individual performances were compared on different artificial clustering test instances and real image segmentation tasks. However, little focus has been placed on the issue that how about their combinational performances are, although multi-objective optimization has attracted much interest from current researchers in unsupervised classification. Here, we firstly evaluate the performance of five state-of-the-art clustering indices with different characteristics in a single-objective optimization algorithm designed in artificial immune system (AIS). Then, a multi-objective optimization algorithm in AIS with fair ability of adaptability and diversity maintaining is introduced in this study. After that, the clustering performances of combinational clustering indices are investigated in the multi-objective optimization framework. To test the effectiveness of the combinational clustering validity indices, 27 benchmark functions with different geometric structures and three complicated remote sensing images are employed in this study. Based on the computer simulations, some meaningful empirical guidelines are obtained for selecting the suitable combinational clustering indices for formulating an effective and robust multi-objective clustering algorithm in different clustering tasks.

关键词： artificial immune system multi-objective clustering combinations of validity measures

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Radio & BH Load-Aware multi-objective clustering in multi-Cell MIMO Cooperative Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第5期70卷 4585-4600页

作者： Bassoy, Selcuk Jaber, Mona Onireti, Oluwakayode Imran, Muhammad A. Telefon UK Ltd Slough SL1 4DX Berks England Queen Mary Univ London Sch Elect Engn & Comp Sci London E1 4FZ England Univ Glasgow James Watt Sch Engn Glasgow G12 8QQ Lanark Scotland Ajman Univ Coll Engn & Informat Technol Artificial Intelligence Res Ctr Ajman U Arab Emirates

Coordinated multipoint (CoMP) is one of the key technologies identified for future wireless networks to mitigate inter-cell interference, especially in a dense deployment scenario. However, CoMP can't be realized for the whole network due to its computational complexity, synchronization between coordinating base stations (BSs) and high backhaul (BH) capacity requirement. BSs need to be clustered into smaller groups and CoMP can be activated within these smaller clusters. In this paper, we develop a multi-objective, dynamic clustering model for multi-user, joint-transmission CoMP to jointly optimize spectral efficiency (SE), radio access network (RAN) load and BH load. We formulate our load-aware model as two coalitional sub-games for small cell and user equipment clustering, respectively. Merge/split/transfer actions for each sub-game are defined and a complexity and stability analysis is provided. Extensive simulation results show that our model provides as good SE in low load when compared to a greedy model, and significantly better load balancing with a reduced number of unsatisfied users and increased throughput in high load scenario. On average 49% increase in the overall system throughput is observed in our simulations when compared to the greedy model.

关键词： Load modeling Throughput 5G mobile communication Radio access networks Precoding Interference Heuristic algorithms Backhaul-aware coalitional sub-games coordinated multipoint system load-aware multi-objective clustering

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An analysis of the admissibility of the objective functions applied in evolutionary multi-objective clustering

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INFORMATION SCIENCES 2022年 610卷 1143-1162页

作者： Morimoto, Cristina Y. Pozo, Aurora de Souto, Marcilio C. P. Federal Univ Parana Curitiba PR Brazil Univ Orleans LIFO Orleans France

A variety of clustering criteria have been applied as an objective function in Evolutionary multi-objective clustering approaches (EMOCs). However, most EMOCs do not provide detailed analysis regarding the choice and usage of the objective functions. In general, the choice of the objective functions only considers the desired clustering properties, and most EMOCs present in the literature do not consider aspects of multi-objective opti-mization, such as the search direction, in their design. Aiming to support a better choice and definition of the objectives in the EMOCs, this paper proposes an analysis of the clus-tering criteria admissibility to examine the search direction and evaluate their potential in finding optimal results. We consider the fundamentals of the evaluation of a heuristic func-tion to analyze the clustering criteria and demonstrate how they can influence the optimization. As a result, this study provides a detailed analysis of the main objective func-tions found in the literature and evaluates how the initialization interferes with their admissibility. Also, we highlight some common practices and issues found in some estab-lished EMOCs. Furthermore, we provide insights regarding how to combine and use the clustering criteria in the EMOCs.(c) 2022 Elsevier Inc. All rights reserved.

关键词： clustering criteria multi-objective clustering Evolutionary multi-objective optimization clustering analysis

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