A Multi-population BRKGA for the Automatic Clustering Problem

Data clustering is a technique that aims to represent a dataset in clusters according to their similarities. In clustering algorithms, it is usually assumed that the number of clusters is known. Unfortunately, the optimal number of clusters is unknown for many applications. This kind of problem is called Automatic Clustering. There are several cluster validity indexes for evaluating solutions, it is known that the quality of a result is influenced by the chosen function. From this, a genetic algorithm is described in this article for the resolution of the automatic clustering using the Calinski-Harabasz Index as a form of evaluation. Comparisons of the results with other algorithms in the literature are also presented. In a first analysis, fitness values equivalent or higher are found in at least 58% of cases for each comparison. Our algorithm can also find the correct number of clusters or close values in 33 cases out of 48. In another comparison, some fitness values are lower, even with the correct number of clusters, but graphically the partitioning are adequate. Thus, it is observed that our proposal is justified and improvements can be studied for cases where the correct number of clusters is not found.

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A genetic approach to the automatic clustering problem

Pattern Recognition ◽

10.1016/s0031-3203(00)00005-4 ◽

2001 ◽

Vol 34 (2) ◽

pp. 415-424 ◽

Cited By ~ 131

Author(s):

Lin Yu Tseng ◽

Shiueng Bien Yang

Keyword(s):

Genetic Approach ◽

Clustering Problem ◽

Automatic Clustering

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A robust dynamic niching genetic algorithm with niche migration for automatic clustering problem

Pattern Recognition ◽

10.1016/j.patcog.2009.10.020 ◽

2010 ◽

Vol 43 (4) ◽

pp. 1346-1360 ◽

Cited By ~ 35

Author(s):

Dong-Xia Chang ◽

Xian-Da Zhang ◽

Chang-Wen Zheng ◽

Dao-Ming Zhang

Keyword(s):

Genetic Algorithm ◽

Clustering Problem ◽

Automatic Clustering

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An Extended Membrane System with Active Membranes to Solve Automatic Fuzzy Clustering Problems

International Journal of Neural Systems ◽

10.1142/s0129065716500040 ◽

2016 ◽

Vol 26 (03) ◽

pp. 1650004 ◽

Cited By ~ 36

Author(s):

Hong Peng ◽

Jun Wang ◽

Peng Shi ◽

Mario J. Pérez-Jiménez ◽

Agustín Riscos-Núñez

Keyword(s):

Fuzzy Clustering ◽

Membrane Structure ◽

Membrane System ◽

Benchmark Problems ◽

Clustering Methods ◽

Clustering Problem ◽

Automatic Clustering ◽

Active Membranes ◽

Comparison Results ◽

Fuzzy Clustering Method

This paper focuses on automatic fuzzy clustering problem and proposes a novel automatic fuzzy clustering method that employs an extended membrane system with active membranes that has been designed as its computing framework. The extended membrane system has a dynamic membrane structure; since membranes can evolve, it is particularly suitable for processing the automatic fuzzy clustering problem. A modification of a differential evolution (DE) mechanism was developed as evolution rules for objects according to membrane structure and object communication mechanisms. Under the control of both the object’s evolution-communication mechanism and the membrane evolution mechanism, the extended membrane system can effectively determine the most appropriate number of clusters as well as the corresponding optimal cluster centers. The proposed method was evaluated over 13 benchmark problems and was compared with four state-of-the-art automatic clustering methods, two recently developed clustering methods and six classification techniques. The comparison results demonstrate the superiority of the proposed method in terms of effectiveness and robustness.

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Multi-Start Jaya Algorithm for Software Module Clustering Problem

Azerbaijan Journal of High Performance Computing ◽

10.32010/26166127.2018.1.1.87.112 ◽

2018 ◽

Vol 1 (1) ◽

pp. 87-112 ◽

Cited By ~ 1

Author(s):

Kamal Z. Zamli ◽

◽

Abdulrahman Alsewari ◽

Bestoun S. Ahmed ◽

◽

...

Keyword(s):

Jaya Algorithm ◽

Software Module ◽

Clustering Problem ◽

Software Module Clustering

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An Improved B-hill Climbing Optimization Technique for Solving the Text Documents Clustering Problem

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405614666180903112541 ◽

2020 ◽

Vol 16 (4) ◽

pp. 296-306 ◽

Cited By ~ 3

Author(s):

Laith Mohammad Abualigah ◽

Essam Said Hanandeh ◽

Ahamad Tajudin Khader ◽

Mohammed Abdallh Otair ◽

Shishir Kumar Shandilya

Keyword(s):

Optimization Technique ◽

Document Clustering ◽

Text Clustering ◽

Hill Climbing ◽

Text Documents ◽

Clustering Problem ◽

Text Document ◽

Text Information ◽

Amount Of Knowledge ◽

The Hill

Background: Considering the increasing volume of text document information on Internet pages, dealing with such a tremendous amount of knowledge becomes totally complex due to its large size. Text clustering is a common optimization problem used to manage a large amount of text information into a subset of comparable and coherent clusters. Aims: This paper presents a novel local clustering technique, namely, β-hill climbing, to solve the problem of the text document clustering through modeling the β-hill climbing technique for partitioning the similar documents into the same cluster. Methods: The β parameter is the primary innovation in β-hill climbing technique. It has been introduced in order to perform a balance between local and global search. Local search methods are successfully applied to solve the problem of the text document clustering such as; k-medoid and kmean techniques. Results: Experiments were conducted on eight benchmark standard text datasets with different characteristics taken from the Laboratory of Computational Intelligence (LABIC). The results proved that the proposed β-hill climbing achieved better results in comparison with the original hill climbing technique in solving the text clustering problem. Conclusion: The performance of the text clustering is useful by adding the β operator to the hill climbing.

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Segmentation of SAR Image using Fuzzy C-Means and Filters

Science & Technology Journal ◽

10.22232/stj.2020.08.01.11 ◽

2020 ◽

Vol 8 (1) ◽

pp. 84-90

Author(s):

R. Lalchhanhima ◽

◽

Debdatta Kandar ◽

R. Chawngsangpuii ◽

Vanlalmuansangi Khenglawt ◽

...

Keyword(s):

Clustering Algorithm ◽

State Of The Art ◽

Speckle Noise ◽

Synthetic Aperture Radar Image ◽

Synthetic Aperture ◽

Sar Image ◽

Spatial Filters ◽

Fuzzy C Means ◽

Automatic Clustering ◽

Intensity Information

Fuzzy C-Means is an unsupervised clustering algorithm for the automatic clustering of data. Synthetic Aperture Radar Image Segmentation has been a challenging task because of the presence of speckle noise. Therefore the segmentation process can not directly rely on the intensity information alone but must consider several derived features in order to get satisfactory segmentation results. In this paper, it is attempted to use the fuzzy nature of classification for the purpose of unsupervised region segmentation in which FCM is employed. Different features are obtained by filtering of the image by using different spatial filters and are selected for segmentation criteria. The segmentation performance is determined by the accuracy compared with a different state of the art techniques proposed recently.

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