A new Differential Evolution based Fuzzy Clustering for Automatic Cluster Evolution

Saha, Indrajit; Maulik, Ujjwal; Bandyopadhyay, Sanghamitra

doi:10.1109/iadcc.2009.4809099

Cited by 13 publications

(8 citation statements)

References 12 publications

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“…The authors also reported the application of the ACDE algorithm to the automatic segmentation of images. Then, in [39], a new hybrid algorithm based on differential evaluation and fuzzy clustering called ADEFC has been proposed to solve the automatic clustering problem. In this algorithm, the cluster heads are encoded in the vectors.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Data Clustering Framework Using Nature-Inspired Binary Optimization Algorithms

Merikhi

Soleymani

2021

IEEE Access

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Cluster analysis using metaheuristic algorithms has earned increasing popularity and acceptance over recent years due to the great success of these algorithms in finding high-quality clusters in complex real-world problems. This paper proposes a novel framework for automatic data clustering with the capability of generating clusters with approximately the same maximum distortion using nature-inspired binary optimization algorithms. The inherent problem with clustering using such algorithms is having a huge search space. Therefore, we propose a binary encoding scheme for the particle representation to alleviate this problem as well. The proposed clustering solution requires no prior knowledge of the number of clusters and proceed with the process based on re-clustering, merging, and modifying the small clusters to compensate for the distortion gap between groups with different sizes. The proposed framework's performance has been evaluated over a wide range of synthetic, real-life, and higher dimensional datasets first by considering four different binary optimization algorithms for the optimizer module. Then, it has been compared with multiple classical and new clustering solutions in addition to two automatic clustering techniques using continuous search space in terms of separation and compactness of the clusters by utilizing internal validity measures. The experimental results show the proposed solution is highly efficient in creating well-separated and compact clusters with approximately the same distortion. Moreover, the application of the proposed framework to the correlated binary data is also reported as a case study. The presence of correlation in the binary dataset results from the similarity between data in the same category, such as repeated measurements in remote sensing, crowdsourced multi-view video uploading, and augmented reality. The simplicity, customizability, and flexibility to add additional conditions to the proposed solution in addition to having a dynamic number of clusters are the advantages of the proposed framework.

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Section: Literature Reviewmentioning

confidence: 99%

Automatic Data Clustering Framework Using Nature-Inspired Binary Optimization Algorithms

Merikhi

Soleymani

2021

IEEE Access

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“…Based on the Shannon entropy and fuzzy variation theory, Zhang and Jiang put forward a new fuzzy clustering validity index taking account of the geometry structure of the dataset [10]. Saha et al put forward an algorithm based on differential evolution for automatic cluster detection, which well evaluated the validity of the clustering result [11]. Yue et al partitioned the original data space into a grid-based structure and proposed a cluster separation measure based on grid distances [12].…”

Section: Related Workmentioning

confidence: 99%

A Self-Adaptive Fuzzyc-Means Algorithm for Determining the Optimal Number of Clusters

Ren

Liu

Wang

et al. 2016

Computational Intelligence and Neuroscience

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For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule n and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result.

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“…The well-known intrinsic relationship between clustering and optimization has attracted the attention of researchers in the field of meta-heuristic optimization to the problem of estimating k for any given dataset. Numerous research papers have been published where all kinds of evolutionary algorithms are used for tackling the problem of estimating k [2], [10], [36], and [42] and the problem of finding an appropriate clustering of data into exactly k clusters [1] [21], [28], [33], and [25]. In this paper we try to provide a new best-performing algorithm, to the best of our knowledge, for the same task.…”

Section: Clustering Problemmentioning

confidence: 99%

“…Apparently, as a response to the previous paper, [36] presented another differential evolution algorithm called ADEFC (A new Differential Evolution based on Fuzzy Clustering) for finding the appropriate number of clusters in a fuzzy dataset. Almost with the same genotype coding explained before, ADEFC encodes each cluster representative with real numbers, but with an extra array of bits for activating/deactivating each representative.…”

Section: Finding Optimal Value For Kmentioning

confidence: 99%

Efficiently Finding the Optimum Number of Clusters in a Dataset with a New Hybrid Cellular Evolutionary Algorithm

Arellano‐Verdejo¹,

Guzmán-Arenas²,

Godoy-Calderón³

et al. 2014

CyS

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A challenge in hybrid evolutionary algorithms is to employ efficient strategies to cover all the search space, applying local search only in actually promising search areas; on the other hand, clustering algorithms, a fundamental base for data mining procedures and learning techniques, suffer from the lack of efficient methods for determining the optimal number of clusters to be found in an arbitrary dataset. Some existing methods use evolutionary algorithms with cluster validation index as the objective function. In this article, a new cellular evolutionary algorithm based on a hybrid model of global and local heuristic search is proposed for the same task, and extensive experimentation is done with different datasets and indexes.

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A new Differential Evolution based Fuzzy Clustering for Automatic Cluster Evolution

Cited by 13 publications

References 12 publications

Automatic Data Clustering Framework Using Nature-Inspired Binary Optimization Algorithms

Automatic Data Clustering Framework Using Nature-Inspired Binary Optimization Algorithms

A Self-Adaptive Fuzzyc-Means Algorithm for Determining the Optimal Number of Clusters

Efficiently Finding the Optimum Number of Clusters in a Dataset with a New Hybrid Cellular Evolutionary Algorithm

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