A Novel Hybrid Clustering Analysis Based on Combination of K-Means and PSO Algorithm (original) (raw)
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Improving the Cluster Performance by Combining Pso and K-Means Algorithm
ICTACT Journal on Soft Computing, 2011
Clustering is a technique that can divide data objects into groups based on information found in the data that describes the objects and their relationships. In this paper describe to improving the clustering performance by combine Particle Swarm Optimization (PSO) and Kmeans algorithm. The PSO algorithm successfully converges during the initial stages of a global search, but around global optimum, the search process will become very slow. On the contrary, K-means algorithm can achieve faster convergence to optimum solution. Unlike K-means method, new algorithm does not require a specific number of clusters given before performing the clustering process and it is able to find the local optimal number of clusters during the clustering process. In each iteration process, the inertia weight was changed based on the current iteration and best fitness. The experimental result shows that better performance of new algorithm by using different data sets.
Comparative Study of Particle Swarm Optimization based Unsupervised Clustering Techniques
2009
In order to overcome the shortcomings of traditional clustering algorithms such as local optima and sensitivity to initialization, a new Optimization technique, Particle Swarm Optimization is used in association with Unsupervised Clustering techniques in this paper. This new algorithm uses the capacity of global search in PSO algorithm and solves the problems associated with traditional clustering techniques. This merge avoids the local optima problem and increases the convergence speed. Parameters, time, distance and mean, are used to compare PSO based Fuzzy C-Means, PSO based Gustafson's-Kessel, PSO based Fuzzy K-Means with extragrades and PSO based K-Means are suitably plotted. Thus, Performance evaluation of Particle Swarm Optimization based Clustering techniques is achieved. Results of this PSO based clustering algorithm is used for remote image classification. Finally, accuracy of this image is computed along with its Kappa Coefficient.
A Novel Hybrid Clustering Techniques based on K-Means, PSO and Dynamic Optimization
International Journal of Computer Applications
Clustering is a process for partitioning datasets. This technique is a challenging field of research in which their potential applications pose their own special requirements. K-Means is the most extensively used algorithm to find a partition that minimizes Mean Square Error (MSE) is an exigent task. The Object Function of the K-Means is not convex and hence it may contain local minima. ACO methods are useful in problems that need to find paths to goals. Particle swarm optimization (PSO) is a global optimization algorithm for dealing with problems in which a best solution can be represented as a point or surface in an n-dimensional space. But PSO algorithm suffers from slow convergence near optimal solution. In this paper a new modified sequential clustering approach is proposed, which uses PSO in combination with K-Means & dynamic optimization algorithm for data clustering. This approach overcomes drawbacks of K-means, PSO technique, improves clustering and avoids being trapped in a local optimal solution. It was ascertained that the K-Means, PSO, KPSOK & dynamic optimization algorithms are proposed among these algorithms dynamic optimization results in accurate, robust and better clustering.
Improve Hybrid Particle Swarm Optimization and K-Means for Clustering
Journal of Information Technology and Computer Science
This research was conducted in Batu city, by classifying land based on land suitability for potato crops. Batu city is a hilly area with a high land slope so that there is a high potential for land degradation. Potato crop production is influenced by climate, suitability of planting land and treatment before harvest. Based on these problems, land mapping is needed so that it is easier for farmers to determine the optimal planting location for potato crops. The land mapping process is carried out using clustering techniques. The clustering process is carried out using 11 land suitability criteria for potato crops including average temperature, first month rainfall, second and third month rainfall, fourth month rainfall, drainage, soil texture, soil depth, Ph H2O, C-Organic, CEC and slope. The clustering results are 4 land suitability classes which are very suitable (S1), suitable (S2), quite suitable (S3) and not suitable (N). The clustering process is carried out using 5 different architectures namely K-Means, Particle swarm optimization (PSO), K-Means PSO, PSO K-Means, and Particle Swarm Optimization and K-Means (KCPSO) hybrids. The fitness value is calculated using the silhouette coefficient calculation. Architectural testing is done to get an architecture that has the highest fitness value. In this study a new approach was used to improve the accuracy of clustering results in the KCPSO architecture using the random injection method. Based on the test results, the KCPSO architecture obtained the biggest fitness values compared to the other fife clustering architectures. Testing the results of clustering is done by comparing the results of the KCPSO method with expert calculations.
Performance Comparisons of PSO based Clustering
Computing Research Repository, 2010
In this paper we have investigated the performance of PSO Particle Swarm Optimization based clustering on few real world data sets and one artificial data set. The performances are measured by two metric namely quantization error and inter-cluster distance. The K means clustering algorithm is first implemented for all data sets, the results of which form the basis of comparison of PSO based approaches. We have explored different variants of PSO such as gbest, lbest ring, lbest vonneumann and Hybrid PSO for comparison purposes. The results reveal that PSO based clustering algorithms perform better compared to K means in all data sets.
Swarm and Evolutionary Computation, 2014
Optimization based pattern discovery has emerged as an important field in knowledge discovery and data mining (KDD), and has been used to enhance the efficiency and accuracy of clustering, classification, association rules and outlier detection. Cluster analysis, which identifies groups of similar data items in large datasets, is one of its recent beneficiaries. The increasing complexity and large amounts of data in the datasets have seen data clustering emerge as a popular focus for the application of optimization based techniques. Different optimization techniques have been applied to investigate the optimal solution for clustering problems. Swarm intelligence (SI) is one such optimization technique whose algorithms have successfully been demonstrated as solutions for different data clustering domains. In this paper we investigate the growth of literature in SI and its algorithms, particularly Particle Swarm Optimization (PSO). This paper makes two major contributions. Firstly, it provides a thorough literature overview focusing on some of the most cited techniques that have been used for PSO-based data clustering. Secondly, we analyze the reported results and highlight the performance of different techniques against contemporary clustering techniques. We also provide an brief overview of our PSO-based hierarchical clustering approach (HPSO-clustering) and compare the results with traditional hierarchical agglomerative clustering (HAC), K-means, and PSO clustering.
Data clustering is an approach for automatically finding classes, concepts, or groups of patterns. It also aims at representing large datasets by a few number of prototypes or clusters. It brings simplicity in modelling data and plays an important role in the process of knowledge discovery and data mining. Data mining tasks require fast and accurate partitioning of huge datasets, which may come with a variety of attributes or features. This imposes computational requirements on the clustering techniques. Swarm Intelligence (SI) has emerged that meets these requirements and has successfully been applied to a number of real world clustering problems. This paper looks into the use of Particle Swarm Optimization for cluster analysis. The effectiveness of Fuzzy C-means clustering provides enhanced performance and maintains more diversity in the swarm and allows the particles to be robust to trace the changing environment. Data structure identifying from the large scale data has become a very important in the data mining problems. Cluster analysis identifies groups of similar data items in large datasets which is one of its recent beneficiaries. The increasing complexity and large amounts of data in the data sets that have seen data clustering emerge as a popular focus for the application of optimization based techniques. Different optimization techniques have been applied to investigate the optimal solution for clustering problems. This paper also proposes two new approaches using PSO to cluster data. It is shown how PSO can be used to find the centroids of a user specified number of clusters.
A particle swarm optimization approach to clustering
Expert Systems with Applications, 2011
The clustering problem has been studied by many researchers using various approaches, including tabu searching, genetic algorithms, simulated annealing, ant colonies, a hybridized approach, and artificial bee colonies. However, almost none of these approaches have employed the pure particle swarm optimization (PSO) technique. This study presents a new PSO approach to the clustering problem that is effective, robust, comparatively efficient, easy-to-tune and applicable when the number of clusters is either known or unknown. The algorithm was tested using two artificial and five real data sets. The results show that the algorithm can successfully solve both clustering problems with both known and unknown numbers of clusters.