K-harmonic means data clustering with simulated annealing heuristic

2007 ◽  
Vol 184 (2) ◽  
pp. 199-209 ◽  
Author(s):  
Zülal Güngör ◽  
Alper Ünler
Keyword(s):  
Simulated Annealing ◽  
Data Clustering ◽  
Harmonic Means

scholarly journals Improved Bidirectional CABOSFV Based on Multi-Adjustment Clustering and Simulated Annealing

2016 ◽  
Vol 16 (6) ◽  
pp. 27-42 ◽  
Author(s):  
Minghan Yang ◽  
Xuedong Gao ◽  
Ling Li
Keyword(s):  
Simulated Annealing ◽  
Data Clustering ◽  
Time Complexity ◽  
Clustering Algorithm ◽  
Feature Vector ◽  
Parameter Determination ◽  
Data Sets ◽  
Parameter Vector ◽  
Clustering Validity

Abstract Although Clustering Algorithm Based on Sparse Feature Vector (CABOSFV) and its related algorithms are efficient for high dimensional sparse data clustering, there exist several imperfections. Such imperfections as subjective parameter designation and order sensibility of clustering process would eventually aggravate the time complexity and quality of the algorithm. This paper proposes a parameter adjustment method of Bidirectional CABOSFV for optimization purpose. By optimizing Parameter Vector (PV) and Parameter Selection Vector (PSV) with the objective function of clustering validity, an improved Bidirectional CABOSFV algorithm using simulated annealing is proposed, which circumvents the requirement of initial parameter determination. The experiments on UCI data sets show that the proposed algorithm, which can perform multi-adjustment clustering, has a higher accurateness than single adjustment clustering, along with a decreased time complexity through iterations.

scholarly journals A New Method on Data Clustering Based on Hybrid K-Harmonic Means and Imperialist Competitive Algorithm

2013 ◽  
Vol 10 (7) ◽  
pp. 1848-1857
Author(s):  
Marjan Abdeyazdan
Keyword(s):  
Data Clustering ◽  
Particle Swarm Optimization Algorithm ◽  
Clustering Algorithms ◽  
Optimization Technique ◽  
Imperialist Competitive Algorithm ◽  
Swarm Optimization ◽  
Local Optima ◽  
Evolutionary Genetic ◽  
Harmonic Means ◽  
Global Optimization Technique

Data clustering is one of the commonest data mining techniques. The K-means algorithm is one of the most wellknown clustering algorithms thatare increasingly popular due to the simplicity of implementation and speed of operation. However, its performancecouldbe affected by some issues concerningsensitivity to the initialization and getting stuck in local optima. The K-harmonic means clustering method manages the issue of sensitivity to initialization but the local optimaissue still compromises the algorithm. Particle Swarm Optimization algorithm is a stochastic global optimization technique which is a good solution to the above-mentioned problems. In the present article, the PSOKHM, a hybrid algorithm which draws upon the advantages of both of the algorithms, strives not only to overcome the issue of local optima in KHM but also the slow convergence speed of PSO. In this article, the proposed GSOKHM method, which is a combination of PSO and the evolutionary genetic algorithmwithin PSOKHM,has been positedto enhancethe PSO operation. To carry out this experiment, four real datasets have been employed whose results indicate thatGSOKHMoutperforms PSOKHM.

Data Clustering Based on an Efficient Hybrid of K-Harmonic Means, PSO and GA

2011 ◽  
pp. 125-140 ◽  
Author(s):  
Malihe Danesh ◽  
Mahmoud Naghibzadeh ◽  
Mohammad Reza Akbarzadeh Totonchi ◽  
Mohaddeseh Danesh ◽  
Behrouz Minaei ◽  
...  
Keyword(s):  
Data Clustering ◽  
Harmonic Means
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