A Hybrid Artificial Fish Swarm Simulated Annealing Optimization Algorithm for Automatic Identification of Clusters

2016 ◽  
Vol 15 (05) ◽  
pp. 949-974 ◽  
Author(s):  
Si He ◽  
Nabil Belacel ◽  
Alan Chan ◽  
Habib Hamam ◽  
Yassine Bouslimani
Keyword(s):  
Simulated Annealing ◽  
Fuzzy Clustering ◽  
A Priori ◽  
Clustering Algorithms ◽  
Hybrid Approach ◽  
Real Life ◽  
Automatic Identification ◽  
Artificial Fish Swarm Algorithm ◽  
Fuzzy Clustering Method ◽  
Artificial Fish Swarm

This paper introduces an alternative fuzzy clustering method that does not require fixing the number of clusters a priori and produce reliable clustering results. This newly proposed method empowers the existing Improved Artificial Fish Swarm algorithm (IAFSA) by the simulated annealing (SA) algorithm. The hybrid approach can prevent IAFSA from unexpected vibration and accelerate convergence rate in the late stage of evolution. Computer simulations are performed to compare this new method with well-known fuzzy clustering algorithms using several synthetic and real-life datasets. Our experimental results show that our newly proposed approach outperforms some other well-known existing fuzzy clustering algorithms in terms of both accuracy and robustness.

scholarly journals An Adaptive Multiobjective Genetic Algorithm with Fuzzy c-Means for Automatic Data Clustering

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Ze Dong ◽  
Hao Jia ◽  
Miao Liu
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Majority Vote ◽  
Clustering Algorithms ◽  
Nsga Ii ◽  
Number Of Clusters ◽  
Automatic Data ◽  
Multiobjective Genetic Algorithm ◽  
Fuzzy Clustering Method

This paper presents a fuzzy clustering method based on multiobjective genetic algorithm. The ADNSGA2-FCM algorithm was developed to solve the clustering problem by combining the fuzzy clustering algorithm (FCM) with the multiobjective genetic algorithm (NSGA-II) and introducing an adaptive mechanism. The algorithm does not need to give the number of clusters in advance. After the number of initial clusters and the center coordinates are given randomly, the optimal solution set is found by the multiobjective evolutionary algorithm. After determining the optimal number of clusters by majority vote method, the Jm value is continuously optimized through the combination of Canonical Genetic Algorithm and FCM, and finally the best clustering result is obtained. By using standard UCI dataset verification and comparing with existing single-objective and multiobjective clustering algorithms, the effectiveness of this method is proved.

scholarly journals Extracting physical homogeneous regions out of irrigation networks using fuzzy clustering method: a case study for the Ghazvin canal irrigation network

2010 ◽  
Vol 13 (4) ◽  
pp. 652-660 ◽  
Author(s):  
M. J. Monem ◽  
S. M. Hashemy
Keyword(s):  
Fuzzy Clustering ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Spatial Diversity ◽  
Optimal Number ◽  
Irrigation Network ◽  
Irrigation Networks ◽  
Fuzzy Clustering Method ◽  
Physical Attributes ◽  
Homogeneous Regions

Improving the current operation and maintenance activities is one of the main steps in achieving higher performance of irrigation networks. Improving the irrigation network management, influenced by different spatial and temporal parameters, is confronted with special difficulties. One of the controversial issues often faced by decision-makers is how to cope with the spatial diversity of irrigation systems. Homogeneous area detection out of the irrigation networks could improve the current management of networks. The idea behind this research is to present a quantitative benchmark for exploring the homogeneous areas with similar physical attributes out of the network region. Five physical attributes, such as length, capacity, number of intakes, number of conveyance structures and the covered irrigated area for each canal reach, are used for spatial clustering. Two fuzzy clustering algorithms, namely FCM and GK, are applied to the Ghazvin irrigation network. Using a clustering validity index, SC, shows that the GK algorithm is the more appropriate tool for clustering of the considered dataset. According to the results the optimal number of clusters for the Ghazvin irrigation project is derived as nine clusters and the irrigated district is classified into nine homogeneous areas. Physical homogeneous regions provide a context for better and easier decision-making.

scholarly journals Incremental Interval Type-2 Fuzzy Clustering of Data Streams using Single Pass Method

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3210
Author(s):  
Sana Qaiyum ◽  
Izzatdin Aziz ◽  
Mohd Hilmi Hasan ◽  
Asif Irshad Khan ◽  
Abdulmohsen Almalawi
Keyword(s):  
Fuzzy Clustering ◽  
Data Streams ◽  
Clustering Algorithms ◽  
Real Life ◽  
Fuzzy Cluster ◽  
Optimization Approach ◽  
Cluster Validity Index ◽  
Data Points ◽  
Interval Type

Data Streams create new challenges for fuzzy clustering algorithms, specifically Interval Type-2 Fuzzy C-Means (IT2FCM). One problem associated with IT2FCM is that it tends to be sensitive to initialization conditions and therefore, fails to return global optima. This problem has been addressed by optimizing IT2FCM using Ant Colony Optimization approach. However, IT2FCM-ACO obtain clusters for the whole dataset which is not suitable for clustering large streaming datasets that may be coming continuously and evolves with time. Thus, the clusters generated will also evolve with time. Additionally, the incoming data may not be available in memory all at once because of its size. Therefore, to encounter the challenges of a large data stream environment we propose improvising IT2FCM-ACO to generate clusters incrementally. The proposed algorithm produces clusters by determining appropriate cluster centers on a certain percentage of available datasets and then the obtained cluster centroids are combined with new incoming data points to generate another set of cluster centers. The process continues until all the data are scanned. The previous data points are released from memory which reduces time and space complexity. Thus, the proposed incremental method produces data partitions comparable to IT2FCM-ACO. The performance of the proposed method is evaluated on large real-life datasets. The results obtained from several fuzzy cluster validity index measures show the enhanced performance of the proposed method over other clustering algorithms. The proposed algorithm also improves upon the run time and produces excellent speed-ups for all datasets.

scholarly journals A Hybrid Method for Image Segmentation Based on Artificial Fish Swarm Algorithm and Fuzzyc-Means Clustering

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Li Ma ◽  
Yang Li ◽  
Suohai Fan ◽  
Runzhu Fan
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Clustering Algorithms ◽  
Optimal Solution ◽  
Medical Image Segmentation ◽  
Superior Result ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm ◽  
Fcm Clustering ◽  
Swarm Algorithm

Image segmentation plays an important role in medical image processing. Fuzzyc-means (FCM) clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artificial fish swarm algorithm (HAFSA). The proposed algorithm combines artificial fish swarm algorithm (AFSA) with FCM whose advantages of global optimization searching and parallel computing ability of AFSA are utilized to find a superior result. Meanwhile, Metropolis criterion and noise reduction mechanism are introduced to AFSA for enhancing the convergence rate and antinoise ability. The artificial grid graph and Magnetic Resonance Imaging (MRI) are used in the experiments, and the experimental results show that the proposed algorithm has stronger antinoise ability and higher precision. A number of evaluation indicators also demonstrate that the effect of HAFSA is more excellent than FCM and suppressed FCM (SFCM).

A New Algorithm for Fuzzy Clustering Handling Incomplete Dataset

2014 ◽  
Vol 23 (04) ◽  
pp. 1460012 ◽  
Author(s):  
Balkis Abidi ◽  
Sadok Ben Yahia
Keyword(s):  
Fuzzy Clustering ◽  
A Priori ◽  
Real Life ◽  
Optimal Number ◽  
Statistical Criterion ◽  
Clustering Methods ◽  
Number Of Clusters ◽  
Missing Value ◽  
Value Estimation ◽  
Optimal Number Of Clusters

One of the most difficult problems in cluster analysis is the identification of the number of groups in a dataset especially in the presence of missing value. Since traditional clustering methods assumed the real number of clusters to be known. However, in real world applications the number of clusters is generally not known a priori. Also, most of clustering methods were developed to analyse complete datasets, they cannot be applied to many practical problems, e.g., on incomplete data. This paper focuses, first, on an algorithm of a fuzzy clustering approach, called OCS-FSOM. The proposed algorithm is based on neural network and uses Optimal Completion Strategy for missing value estimation in incomplete dataset. Then, we propose an extension of our algorithm, to tackle the problem of estimating the number of clusters, by using a multi level OCS-FSOM method. The new algorithm called Multi-OCSFSOM is able to find the optimal number of clusters by using a statistical criterion, that aims at measuring the quality of obtained partitions. Carried out experiments on real-life datasets highlights a very encouraging results in terms of exact determination of optimal number of clusters.

scholarly journals Bearing condition degradation assessment based on basic scale entropy and Gath-Geva fuzzy clustering

2018 ◽  
Vol 10 (10) ◽  
pp. 168781401880353 ◽  
Author(s):  
Bing Wang ◽  
Xiong Hu ◽  
Dejian Sun ◽  
Wei Wang
Keyword(s):  
Fuzzy Clustering ◽  
Clustering Algorithms ◽  
Three Dimensional ◽  
Degradation Process ◽  
Performance Degradation ◽  
Mechanical Equipment ◽  
Time Aggregation ◽  
Fuzzy Clustering Method ◽  
Basic Scale ◽  
Fuzzy C Means Clustering

A method based on basic scale entropy and Gath-Geva fuzzy clustering is proposed in order to solve the issue of bearing degradation condition recognition. The evolution rule of basic scale entropy for bearing in performance degradation process is analyzed first, and the monotonicity and sensitivity of basic scale entropy are emphasized. Considering the continuity of the bearing degradation condition at the time scale, three-dimensional degradation eigenvectors are constructed including basic scale entropy, root mean square, and degradation time, and then, Gath-Geva fuzzy clustering method is used to divide different conditions in performance degradation process, thus realizing performance degradation recognition for bearing. Bearing whole lifetime data from IEEE PHM 2012 is adopted in application and discussion, and fuzzy c-means clustering and Gustafson–Kessel clustering algorithms are analyzed for comparison. The results show that the proposed basic scale entropy-Gath-Geva method has better clustering effect and higher time aggregation than the other two algorithms and is able to provide an effective way for mechanical equipment performance degradation recognition.

Sign in / Sign up

Export Citation Format

Share Document