An Automatic Data Clustering Algorithm Based on Differential Evolution

2013 ◽  
Author(s):  
Chun-Wei Tsai ◽  
Chiech-An Tai ◽  
Ming-Chao Chiang
Keyword(s):  
Differential Evolution ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Automatic Data

Elastic Differential Evolution for Automatic Data Clustering

2019 ◽  
pp. 1-14
Author(s):  
Jun-Xian Chen ◽  
Yue-Jiao Gong ◽  
Wei-Neng Chen ◽  
Mengting Li ◽  
Jun Zhang
Keyword(s):  
Differential Evolution ◽  
Data Clustering ◽  
Automatic Data

Simulation and Application on Data Clustering Based on an Improved FCM Algorithm

2013 ◽  
Vol 380-384 ◽  
pp. 1589-1592
Author(s):  
Xiang Ping Hu
Keyword(s):  
Fault Diagnosis ◽  
Differential Evolution ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Differential Evolution Algorithm ◽  
Optimal Solution ◽  
New Method ◽  
Initial Value ◽  
Fcm Algorithm ◽  
Chaotic Mapping

An improved data clustering algorithm was proposed based on the Fuzzy C-Means (FCM) algorithm for the purpose of clustering the data precisely and effectively, through progressing the performance of the data clustering to afford the element work for the application of fault diagnosis and target recognition and so on. There was fatal weakness for the traditional FCM algorithm that the algorithm is sensitive to initial value and noise. The chaotic differential evolution FCM algorithm was proposed according to the efficient global search capability of differential evolution algorithm and the traversal characteristic of chaotic time series. The improved algorithm used the Logistics chaotic mapping to search for the optimal solution, and the chaos disturbance was introduced into the evolutionary population to make up for the defects of FCM algorithm. The new method can overcome the problems of initial value sensitiveness with FCM and local convergence with genetic algorithm. Because the new method. Three types of typical vibration data of faults engines was taken as the example for the research and application. The simulation and application result shows that the data clustering performance of the improved FCM algorithm is much better than the traditional FCM algorithm, and the accuracy rates of fault diagnosis in the application was increased by more than twenty percent, it shows good application prospect.

scholarly journals K-Means-Based Nature-Inspired Metaheuristic Algorithms for Automatic Data Clustering Problems: Recent Advances and Future Directions

2021 ◽  
Vol 11 (23) ◽  
pp. 11246
Author(s):  
Abiodun M. Ikotun ◽  
Mubarak S. Almutari ◽  
Absalom E. Ezugwu
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Optimization Techniques ◽  
Background Information ◽  
Cluster Number ◽  
Automatic Data ◽  
Partitional Clustering ◽  
Automatic Clustering ◽  
Clustering Problems

K-means clustering algorithm is a partitional clustering algorithm that has been used widely in many applications for traditional clustering due to its simplicity and low computational complexity. This clustering technique depends on the user specification of the number of clusters generated from the dataset, which affects the clustering results. Moreover, random initialization of cluster centers results in its local minimal convergence. Automatic clustering is a recent approach to clustering where the specification of cluster number is not required. In automatic clustering, natural clusters existing in datasets are identified without any background information of the data objects. Nature-inspired metaheuristic optimization algorithms have been deployed in recent times to overcome the challenges of the traditional clustering algorithm in handling automatic data clustering. Some nature-inspired metaheuristics algorithms have been hybridized with the traditional K-means algorithm to boost its performance and capability to handle automatic data clustering problems. This study aims to identify, retrieve, summarize, and analyze recently proposed studies related to the improvements of the K-means clustering algorithm with nature-inspired optimization techniques. A quest approach for article selection was adopted, which led to the identification and selection of 147 related studies from different reputable academic avenues and databases. More so, the analysis revealed that although the K-means algorithm has been well researched in the literature, its superiority over several well-established state-of-the-art clustering algorithms in terms of speed, accessibility, simplicity of use, and applicability to solve clustering problems with unlabeled and nonlinearly separable datasets has been clearly observed in the study. The current study also evaluated and discussed some of the well-known weaknesses of the K-means clustering algorithm, for which the existing improvement methods were conceptualized. It is noteworthy to mention that the current systematic review and analysis of existing literature on K-means enhancement approaches presents possible perspectives in the clustering analysis research domain and serves as a comprehensive source of information regarding the K-means algorithm and its variants for the research community.

Balanced Data Clustering Algorithm for Both Hard and Soft Clustering

2018 ◽  
Vol 6 (2) ◽  
pp. 176-183
Author(s):  
Purnendu Das ◽  
◽  
Bishwa Ranjan Roy ◽  
Saptarshi Paul ◽  
◽  
...  
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Soft Clustering

Tree-ART2 Learning Model for Spatial Clustering in Second Dimension

2014 ◽  
Vol 543-547 ◽  
pp. 1934-1938
Author(s):  
Ming Xiao
Keyword(s):  
Network Model ◽  
Spatial Data ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Spatial Clustering ◽  
Adaptive Resonance Theory ◽  
Spatial Distance ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Vector Module

For a clustering algorithm in two-dimension spatial data, the Adaptive Resonance Theory exists not only the shortcomings of pattern drift and vector module of information missing, but also difficultly adapts to spatial data clustering which is irregular distribution. A Tree-ART2 network model was proposed based on the above situation. It retains the memory of old model which maintains the constraint of spatial distance by learning and adjusting LTM pattern and amplitude information of vector. Meanwhile, introducing tree structure to the model can reduce the subjective requirement of vigilance parameter and decrease the occurrence of pattern mixing. It is showed that TART2 network has higher plasticity and adaptability through compared experiments.

scholarly journals Improved Fuzzy C-Means Clustering for Transformer Fault Diagnosis Using Dissolved Gas Analysis Data

Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2344 ◽  
Author(s):  
Enwen Li ◽  
Linong Wang ◽  
Bin Song ◽  
Siliang Jian
Keyword(s):  
Fault Diagnosis ◽  
Membership Function ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Gas Analysis ◽  
Dissolved Gas ◽  
Fuzzy C Means ◽  
Dissolved Gas Analysis ◽  
Fcm Clustering ◽  
Transformer Fault

Dissolved gas analysis (DGA) of the oil allows transformer fault diagnosis and status monitoring. Fuzzy c-means (FCM) clustering is an effective pattern recognition method, but exhibits poor clustering accuracy for dissolved gas data and usually fails to subsequently correctly classify transformer faults. The existing feasible approach involves combination of the FCM clustering algorithm with other intelligent algorithms, such as neural networks and support vector machines. This method enables good classification; however, the algorithm complexity is greatly increased. In this paper, the FCM clustering algorithm itself is improved and clustering analysis of DGA data is realized. First, the non-monotonicity of the traditional clustering membership function with respect to the sample distance and its several local extrema are discussed, which mainly explain the poor classification accuracy of DGA data clustering. Then, an exponential form of the membership function is proposed to obtain monotony with respect to distance, thereby improving the dissolved gas data clustering. Likewise, a similarity function to determine the degree of membership is derived. Test results for large datasets show that the improved clustering algorithm can be successfully applied for DGA-data-based transformer fault detection.

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