Special Issue "Clustering Algorithms"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (15 August 2015).

Special Issue Editor

Dr. Javier Del Ser Lorente
E-Mail Website1 Website2
Guest Editor
OPTIMA Area, Tecnalia Research & Innovation, 48160 Zamudio, Bizkaia, Spain
Interests: machine learning; meta-heuristic optimization; predictive analytics; resource allocation; wireless network design; cognitive radio; risk contention; logistics; clustering
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The last decade has witnessed an upsurge of new clustering algorithms based on innovative technical approaches beyond those traditionally addressed in the data mining community. While the principles and fundamentals of distance and similarity prevail underneath these new schemes, their pattern search procedures are radically different from previous approaches: among them, evolutionary (and in general, bio-inspired) meta-heuristics have emerged as computationally efficient search engines that can be exploited, so as to find similarity patterns among massive datasets.

This Special Issue invites prospective researchers and practitioners in this field to submit their latest developments and/or surveys of the state of the art concerning the following topics:

- Evolutionary computing for clustering
- Meta-heuristically empowered pattern discovery
- Applications of clustering when hybridized with meta-heuristics
- Other combinations of meta-heuristics and machine learning (e.g., predictive analytics)

Proposals on other related topics will also be considered via a previous query.

Sincerely,

Javier (Javi) Del Ser Lorente
Guest Editor

Keywords

  • Meta-heuristically empowered clustering schemes
  • Meta-heuristics (evolutionary computing, swarm intelligence, etc.) for machine learning

Published Papers (4 papers)

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Open AccessArticle
A New Swarm Intelligence Approach for Clustering Based on Krill Herd with Elitism Strategy
Algorithms 2015, 8(4), 951-964; https://doi.org/10.3390/a8040951 - 22 Oct 2015
Cited by 11
Abstract
As one of the most popular and well-recognized clustering methods, fuzzy C-means (FCM) clustering algorithm is the basis of other fuzzy clustering analysis methods in theory and application respects. However, FCM algorithm is essentially a local search optimization algorithm. Therefore, sometimes, it may [...] Read more.
As one of the most popular and well-recognized clustering methods, fuzzy C-means (FCM) clustering algorithm is the basis of other fuzzy clustering analysis methods in theory and application respects. However, FCM algorithm is essentially a local search optimization algorithm. Therefore, sometimes, it may fail to find the global optimum. For the purpose of getting over the disadvantages of FCM algorithm, a new version of the krill herd (KH) algorithm with elitism strategy, called KHE, is proposed to solve the clustering problem. Elitism tragedy has a strong ability of preventing the krill population from degrading. In addition, the well-selected parameters are used in the KHE method instead of originating from nature. Through an array of simulation experiments, the results show that the KHE is indeed a good choice for solving general benchmark problems and fuzzy clustering analyses. Full article
(This article belongs to the Special Issue Clustering Algorithms)
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Open AccessArticle
Network Community Detection on Metric Space
Algorithms 2015, 8(3), 680-696; https://doi.org/10.3390/a8030680 - 21 Aug 2015
Cited by 2
Abstract
Community detection in a complex network is an important problem of much interest in recent years. In general, a community detection algorithm chooses an objective function and captures the communities of the network by optimizing the objective function, and then, one uses various [...] Read more.
Community detection in a complex network is an important problem of much interest in recent years. In general, a community detection algorithm chooses an objective function and captures the communities of the network by optimizing the objective function, and then, one uses various heuristics to solve the optimization problem to extract the interesting communities for the user. In this article, we demonstrate the procedure to transform a graph into points of a metric space and develop the methods of community detection with the help of a metric defined for a pair of points. We have also studied and analyzed the community structure of the network therein. The results obtained with our approach are very competitive with most of the well-known algorithms in the literature, and this is justified over the large collection of datasets. On the other hand, it can be observed that time taken by our algorithm is quite less compared to other methods and justifies the theoretical findings. Full article
(This article belongs to the Special Issue Clustering Algorithms)
Open AccessArticle
Improving CLOPE’s Profit Value and Stability with an Optimized Agglomerative Approach
Algorithms 2015, 8(3), 380-394; https://doi.org/10.3390/a8030380 - 26 Jun 2015
Cited by 4
Abstract
CLOPE (Clustering with sLOPE) is a simple and fast histogram-based clustering algorithm for categorical data. However, given the same data set with the same input parameter, the clustering results by this algorithm would possibly be different if the transactions are input in a [...] Read more.
CLOPE (Clustering with sLOPE) is a simple and fast histogram-based clustering algorithm for categorical data. However, given the same data set with the same input parameter, the clustering results by this algorithm would possibly be different if the transactions are input in a different sequence. In this paper, a hierarchical clustering framework is proposed as an extension of CLOPE to generate stable and satisfactory clustering results based on an optimized agglomerative merge process. The new clustering profit is defined as the merge criteria and the cluster graph structure is proposed to optimize the merge iteration process. The experiments conducted on two datasets both demonstrate that the agglomerative approach achieves stable clustering results with a better profit value, but costs much more time due to the worse complexity. Full article
(This article belongs to the Special Issue Clustering Algorithms)
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Open AccessArticle
An Optimization Clustering Algorithm Based on Texture Feature Fusion for Color Image Segmentation
Algorithms 2015, 8(2), 234-247; https://doi.org/10.3390/a8020234 - 22 May 2015
Cited by 5
Abstract
We introduce a multi-feature optimization clustering algorithm for color image segmentation. The local binary pattern, the mean of the min-max difference, and the color components are combined as feature vectors to describe the magnitude change of grey value and the contrastive information of [...] Read more.
We introduce a multi-feature optimization clustering algorithm for color image segmentation. The local binary pattern, the mean of the min-max difference, and the color components are combined as feature vectors to describe the magnitude change of grey value and the contrastive information of neighbor pixels. In clustering stage, it gets the initial clustering center and avoids getting into local optimization by adding mutation operator of genetic algorithm to particle swarm optimization. Compared with well-known methods, the proposed method has an overall better segmentation performance and can segment image more accurately by evaluating the ratio of misclassification. Full article
(This article belongs to the Special Issue Clustering Algorithms)
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