The K-means Clustering Algorithm Based on Chaos Particle Swarm
نویسندگان
چکیده
Proposed the Algorithm of K-means (CPSOKM) based on Chaos Particle Swarm in order to solve the problem that K-means algorithm sensitive to initial conditions and is easy to influence the clustering effect. On the selection of the initial value problem, algorithm using particle swarm algorithm to balance the random value uncertainty, and then by introducing a chaotic sequence, the particles move speed and position in a redefined, thus solving the initial value sensitivity, while the algorithm with overall search capability, but also to avoid the local optimum. The algorithm add acceleration factor and escape factor in order to improve the time efficiency. Experiment result proved that the CPSOKM algorithm has a fast convergence speed, high stability, and good clustering effect.
منابع مشابه
Solving Data Clustering Problems using Chaos Embedded Cat Swarm Optimization
In this paper, a new method is proposed for solving the data clustering problem using Cat Swarm Optimization (CSO) algorithm based on chaotic behavior. The problem of data clustering is an important section in the field of the data mining, which has always been noted by researchers and experts in data mining for its numerous applications in solving real-world problems. The CSO algorithm is one ...
متن کاملSolving Data Clustering Problems using Chaos Embedded Cat Swarm Optimization
In this paper, a new method is proposed for solving the data clustering problem using Cat Swarm Optimization (CSO) algorithm based on chaotic behavior. The problem of data clustering is an important section in the field of the data mining, which has always been noted by researchers and experts in data mining for its numerous applications in solving real-world problems. The CSO algorithm is one ...
متن کاملA Hybrid Data Clustering Algorithm Using Modified Krill Herd Algorithm and K-MEANS
Data clustering is the process of partitioning a set of data objects into meaning clusters or groups. Due to the vast usage of clustering algorithms in many fields, a lot of research is still going on to find the best and efficient clustering algorithm. K-means is simple and easy to implement, but it suffers from initialization of cluster center and hence trapped in local optimum. In this paper...
متن کاملA cultural algorithm for data clustering
Clustering is a widespread data analysis and data mining technique in many fields of study such as engineering, medicine, biology and the like. The aim of clustering is to collect data points. In this paper, a Cultural Algorithm (CA) is presented to optimize partition with N objects into K clusters. The CA is one of the effective methods for searching into the problem space in order to find a n...
متن کاملClustering of Fuzzy Data Sets Based on Particle Swarm Optimization With Fuzzy Cluster Centers
In current study, a particle swarm clustering method is suggested for clustering triangular fuzzy data. This clustering method can find fuzzy cluster centers in the proposed method, where fuzzy cluster centers contain more points from the corresponding cluster, the higher clustering accuracy. Also, triangular fuzzy numbers are utilized to demonstrate uncertain data. To compare triangular fuzzy ...
متن کامل