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Comparison Between Single and multi-objective clustering Algorithms: MathE Case Study 1

4th International Conference on Optimization Learning Algorithms and Applications

作者： Azevedo, Beatriz Flamia Rocha, Ana Maria A. C. Fernandes, Florbela P. Pacheco, Maria F. Pereira, Ana I. Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI Braganca Portugal Univ Minho ALGORITMI Res Ctr LASI Campus Gualtar P-4710057 Braga Portugal Univ Aveiro Ctr Res & Dev Math & Applicat CIDMA Aveiro Portugal Inst Politecn Braganca Lab Associado Sustentabilidade & Tecnol Regioes M P-5300253 Braganca Portugal

ISBN: (数字)9783031774263

ISBN: (纸本)9783031774256;9783031774263

This paper compares the results obtained for four single clustering algorithms with a multi-objective clustering approach. For this, a dataset describing the student's behavior within the Linear Algebra topic on the MathE e-learning platform is used. This dataset aids in understanding student performance and engagement in MathE to support the development of an intelligent system to tailor the platform's resources to users's needs. The four algorithms suggested two clusters as the optimal solution for the dataset. However, this binary categorization did not provide meaningful insights into the proposal of the MathE platform;that is, it did not provide a customized system according to individual needs. Thus, this study uses the multi-objective clustering algorithm, which results in a set of non-dominated solutions, providing decision-makers with a broader range of options to choose the solution that best meets their needs. The results demonstrate the main benefits of the proposed human-in-the-loop multi-objective approach since it provides several optimal solutions and allows the decision-maker to apply fundamental knowledge to define the most appropriate solution to the problem based on previous knowledge.

关键词： multi-objective clustering automatic clustering optimization bio-inspired algorithm

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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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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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An adaptive evolutionary multi-objective clustering based on the data properties of the base partitions

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 245卷

作者： Morimoto, Cristina Yassue Pozo, Aurora Trinidad Ramirez de Souto, Marcilio Carlos Pereira Univ Fed Parana Comp Sci Dept Curitiba PR Brazil Univ Orleans Lab Informat Fondamentale Orleans Orleans France

Evolutionary multi -objective clustering (EMOC) is a modern clustering technique in which the general concepts of evolutionary multi -objective optimization are applied to the clustering problem. The design and definition of clustering are difficult problems, and the choice of the objective functions and parameter setting of the algorithms are still challenges. In this paper, we propose the adaptive evolutionary multi -objective clustering approach based on data properties (AEMOC). AEMOC considers a new metric to evaluate the base partitions (candidate solutions used in the initial population) generated by minimum spanning tree clustering. This metric is applied to: (1) determine properties of the data, such as separation and overlap;and (2) define an offline selection of objective functions and parameter settings of the multi -objective algorithm. The information regarding the data properties allows AEMOC to avoid unnecessary data processing in datasets in which the initialization provides optimal solutions and optimization is not required. AEMOC presented promising results considering a diverse set of artificial and real -life datasets, based on two aspects: (1) it succeeded in the determination of the data properties of the base partitions and verifying the potential clustering quality, presenting a correlation of 0.8 with a reference metric, and (2) it provided better clustering results than reference EMOC approaches, achieving a general gain in the clustering performance of 3% in real -life datasets and 7% in artificial datasets.

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

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Detecting Nested Structures Through Evolutionary multi-objective clustering 25th

Detecting Nested Structures Through Evolutionary Multi-objec...

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25th International Conference on the Applications of Evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

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

ISBN: (纸本)9783031024627;9783031024610

The evolutionary multi-objective algorithms have been widely applied for clustering. However, in general, the detection of heterogeneous nested clusters remains challenging for clustering algorithms. This paper proposes an adaptation of the connectedness criterion used as an objective function in established Evolutionary multi-objective clustering approaches (EMOCs). This adaptation can improve the conflict between the objective functions, and then it promotes the detection of nested clusters. We performed experiments with four EMOCs (MOCK, MOCLE, Delta-MOCK, and EMO-KC) that provide different features. These different EMOCs have different initialization methods and representation schemes, allowing us to analyze how the proposed objective function can contribute to detecting nested clusters. Our results show that our adapted objective function promotes a general gain in the performance of all these algorithms.

关键词： multi-objective clustering Nested data clustering Evolutionary multi-objective optimization clustering methods Data mining

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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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Regions of interest selection in histopathological images using subspace and multi-objective stream clustering

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VISUAL COMPUTER 2023年第4期39卷 1683-1701页

作者： Attaoui, Mohammed Oualid Dif, Nassima Azzag, Hanene Lebbah, Mustapha Univ Sorbonne Paris Nord LIPN UMR 7030 Av 99 J-B Clement F-93430 Villetaneuse France Univ Djillali Liabes Sidi Bel Abbes EEDIS Lab Sidi Bel Abbes Algeria

The advances of deep learning in histopathology show the ability to assist pathologists in reducing workload and avoiding subjective decisions. Such algorithms lead to a more reliable diagnosis because they give computer-based second opinions to the clinician. However, in histopathology cancer image analysis, pathologists mostly diagnose the pathology as positive if a small part of it is considered cancer tissue. These small parts are called regions of interest or patches. Finding the relevant patches is crucial as it can save computation time and memory. Also, deep learning systems can receive only small inputs, and these patches represent the best input. This paper proposes a new clustering algorithm for the patch selection based on subspace clustering. This technique discovers clusters embedded in multiple, overlapping subspaces of high-dimensional data. The proposed algorithm manages to find the data's best partitioning and the images' relevant patches.

关键词： Regions of interests selection Histopathological datasets Subspace clustering multi-objective clustering Data stream

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

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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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A multi-objective clustering for Better Data Management in Connected Environment

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BIG DATA RESEARCH 2022年 30卷

作者： Allani, Sabri Chbeir, Richard Salameh, Khouloud Mansour, Elio Arnould, Philippe Univ Pau & Pays Adour LIUPPA E2S UPPA Anglet France Amer Univ Ras Al Khaimah Ras Al Khaymah U Arab Emirates

Over the past decade, the rapid increase in connected devices has enabled the emergence of new digital ecosystems to provide new opportunities for monitoring and managing systems to optimize overall performance. With these connected environments, data collection and management become increasingly challenging. A significant number of works in the literature have addressed data collection and management based on different contexts (e.g., mobile ad hoc, Peer-2-Peer, and IoT networks). Today, a wired network uses all of these protocols simultaneously, thus highlighting the need to build a standard data collection and management framework that considers all potential user preferences. For this purpose, multi-objective clustering has been utilized as a promising solution to ensure the stability of connected devices during the collection and management of data. In this paper, we introduce a new multi-objective clustering (MOC) technique based on various criteria for cluster construction and head selection in connected environments. More precisely, the proposed solution is based hypergraphs to represent the connected environment and clusters according to similarities between heterogeneous devices. Then, a cross-sectional hypergraph algorithm is applied to select the cluster heads. Experiments conducted show that our solution outperforms the pioneering literature methods in terms of performance and effectiveness. (C) 2022 Elsevier Inc. All rights reserved.

关键词： multi-objective clustering Connected environment Data gathering

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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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