Improved search strategies and extensions to k-medoids-based clustering algorithms

Chu, Shu-Chuan; Roddick, John F.; Pan, Jeng-Shyang

doi:10.1504/ijbidm.2008.020520

Cited by 8 publications

(4 citation statements)

References 38 publications

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“…To reduce the computational time required by PAM algorithm and to produce good quality solutions, in [55] it was proposed an algorithm called CLATIN (Clustering Large Applications with Triangular Irregular Network), that uses the concept of the triangular irregular network in the swap procedure of PAM. In [4] PAM algorithm is revisited, and improvements in its swap procedure are proposed. In [33], a fast algorithm that uses the k-means algorithm to define the initial medoids is presented.…”

Section: Related Workmentioning

confidence: 99%

The effective BRKGA algorithm for the k-medoids clustering problem

Brito¹,

Semaan

Fadel

2022

RAIRO-Oper. Res.

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This paper presents a biased random-key genetic algorithm for k-medoids clustering problem. A novel heuristic operator was implemented and combined with a parallelized local search procedure. Experiments were carried out with fifty literature data sets with small, medium, and large sizes, considering several numbers of clusters, showed that the proposed algorithm outperformed eight other algorithms, for example, the classics PAM and CLARA algorithms. Furthermore, with the results of a linear integer programming formulation, we found that our algorithm obtained the global optimal solutions for most cases and, despite its stochastic nature, presented stability in terms of quality of the solutions obtained and the number of generations required to produce such solutions. In addition, considering the solutions (clusterings) produced by the algorithms, a relative validation index (average silhouette) was applied, where, again, was observed that our method performed well, producing cluster with a good structure.

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Section: Related Workmentioning

confidence: 99%

The effective BRKGA algorithm for the k-medoids clustering problem

Brito¹,

Semaan

Fadel

2022

RAIRO-Oper. Res.

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“…To determine the locations of the additional sensors two approaches are proposed inspired by Clustering Large Applications based on Simulated Annealing algorithm (CLASA) [ 37 ] and the Geodesic Distance-based Fuzzy c -Medoid Clustering method (GDFCM) [ 40 ]. In the first algorithm, the CLASA algorithm is modified as follows.…”

Section: Sensor Placement To Ensure Observability and Minimal Relamentioning

confidence: 99%

“…The Clustering Large Applications (CLASA) algorithm [ 37 ] developed as a fast and robust solution to the well-known k -medoid clustering problem. The fundamental idea of the paper is that instead of considering the distance between the cluster centers and the clustered objects, a goal-oriented sensor-placement algorithm can be derived by the introduction of a problem-relevant objective function into the scheme of the CLASA algorithm.…”

Section: Introductionmentioning

confidence: 99%

Network Distance-Based Simulated Annealing and Fuzzy Clustering for Sensor Placement Ensuring Observability and Minimal Relative Degree

Leitold

Vathy-Fogarassy

Abonyi

2018

Sensors

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Network science-based analysis of the observability of dynamical systems has been a focus of attention over the past five years. The maximum matching-based approach provides a simple tool to determine the minimum number of sensors and their positions. However, the resulting proportion of sensors is particularly small when compared to the size of the system, and, although structural observability is ensured, the system demands additional sensors to provide the small relative order needed for fast and robust process monitoring and control. In this paper, two clustering and simulated annealing-based methodologies are proposed to assign additional sensors to the dynamical systems. The proposed methodologies simplify the observation of the system and decrease its relative order. The usefulness of the proposed method is justified in a sensor-placement problem of a heat exchanger network. The results show that the relative order of the observability is decreased significantly by an increase in the number of additional sensors.

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“…Other approaches that have been explored to allow clustering large datasets include: the proposal of new initialization strategies aimed at trying to find a better initial set of medoids, thus enabling faster convergence of the algorithms; the use of the triangle inequality property to speed up the medoid search step; and, in the context of big data, the development of distributed or parallel clustering algorithms, including approaches employing MapReduce, Message Passing Interface (MPI) or GPU‐based strategies …”

Section: Background and Related Workmentioning

confidence: 99%

Open issues for partitioning clustering methods: an overview

Barioni

Razente

Marcelino

et al. 2014

WIREs Data Min & Knowl

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Over the last decades, a great variety of data mining techniques have been developed to reach goals concerning Knowledge Discovery in Databases. Among them, cluster detection techniques are of major importance. Although these techniques have already been largely explored in the scientific literature, there are at least two important open issues: the existent algorithms are not scalable for large high‐dimensional datasets, and the unsupervised nature of traditional data clustering makes it very difficult to generate meaningful clusters. This article presents an overview of the strategies being explored in order to deal more deeply with these issues. Moreover, it describes a new semi‐supervised clustering strategy that exemplifies the integration of several approaches and that can be employed with partitioning algorithms, such as PAM and Clarans. The technique addresses an improvement to these types of algorithms, which is obtained by using must‐link feedback information provided by the users in an interactive and visual environment. WIREs Data Mining Knowl Discov 2014, 4:161–177. doi: 10.1002/widm.1127 This article is categorized under: Technologies > Structure Discovery and Clustering

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Improved search strategies and extensions to k-medoids-based clustering algorithms

Cited by 8 publications

References 38 publications

The effective BRKGA algorithm for the k-medoids clustering problem

The effective BRKGA algorithm for the k-medoids clustering problem

Network Distance-Based Simulated Annealing and Fuzzy Clustering for Sensor Placement Ensuring Observability and Minimal Relative Degree

Open issues for partitioning clustering methods: an overview

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