Ant Colony Cooperative Strategy in Electrocardiogram and Electroencephalogram Data Clustering

2008 ◽  
pp. 323-333 ◽  
Author(s):  
Miroslav Bursa ◽  
Lenka Lhotska
Keyword(s):  
Data Clustering ◽  
Ant Colony ◽  
Cooperative Strategy

Intelligent Ant Colony System for Traveling Salesman Problem and Clustering

2011 ◽  
pp. 18-42
Author(s):  
Shu-Chuan Chu ◽  
Jeng-Shyang Pan
Keyword(s):  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Data Clustering ◽  
Heuristic Algorithms ◽  
Computation Time ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
K Nearest Neighbor ◽  
Ant Colony Systems

Processes that simulate natural phenomena have successfully been applied to a number of problems for which no simple mathematical solution is known or is practicable. Such meta-heuristic algorithms include genetic algorithms, particle swarm optimization and ant colony systems and have received increasing attention in recent years. This work parallelizes the ant colony systems and introduces the communication strategies so as to reduce the computation time and reach the better solution for traveling salesman problem. We also extend ant colony systems and discuss a novel data clustering process using Constrained Ant Colony Optimization (CACO). The CACO algorithm extends the ant colony optimization algorithm by accommodating a quadratic distance metric, the Sum of K Nearest Neighbor Distances (SKNND) metric, constrained addition of pheromone and a shrinking range strategy to improve data clustering. We show that the CACO algorithm can resolve the problems of clusters with arbitrary shapes, clusters with outliers and bridges between clusters

scholarly journals Applying Data Clustering Feature to Speed Up Ant Colony Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Chao-Yang Pang ◽  
Ben-Qiong Hu ◽  
Jie Zhang ◽  
Wei Hu ◽  
Zheng-Chao Shan
Keyword(s):  
Ant Colony Optimization ◽  
Data Clustering ◽  
Optimization Problems ◽  
Ant Colony ◽  
Data Set ◽  
Local Clustering ◽  
Speed Up ◽  
Divide And Rule ◽  
Gene Data

Ant colony optimization (ACO) is often used to solve optimization problems, such as traveling salesman problem (TSP). When it is applied to TSP, its runtime is proportional to the squared size of problemNso as to look less efficient. The following statistical feature is observed during the authors’ long-term gene data analysis using ACO: when the data sizeNbecomes big, local clustering appears frequently. That is, some data cluster tightly in a small area and form a class, and the correlation between different classes is weak. And this feature makes the idea of divide and rule feasible for the estimate of solution of TSP. In this paper an improved ACO algorithm is presented, which firstly divided all data into local clusters and calculated small TSP routes and then assembled a big TSP route with them. Simulation shows that the presented method improves the running speed of ACO by 200 factors under the condition that data set holds feature of local clustering.

Ant colony based hybrid optimization for data clustering

Kybernetes ◽  
2007 ◽  
Vol 36 (2) ◽  
pp. 175-191 ◽  
Author(s):  
Amarendra Nath Sinha ◽  
Nibedita Das ◽  
Gadadhar Sahoo
Keyword(s):  
Data Clustering ◽  
Ant Colony ◽  
Hybrid Optimization

Empirical Studies on Application of Genetic Algorithms and Ant Colony Optimization for Data Clustering

2010 ◽  
Author(s):  
Thelma Elita Colanzi ◽  
Wesley Klewerton Guez Assuncao ◽  
Aurora Trinidad Ramirez Pozo ◽  
Ana Cristina B. Kochem Vendramin ◽  
Diogo Augusto Barros Pereira
Keyword(s):  
Genetic Algorithms ◽  
Ant Colony Optimization ◽  
Data Clustering ◽  
Empirical Studies ◽  
Ant Colony

Study on Information Technology with MADS — A New Data Clustering Algorithm Based on Ant Colony Optimization

2013 ◽  
Vol 859 ◽  
pp. 572-576 ◽  
Author(s):  
Yong Li Liu
Keyword(s):  
Information Technology ◽  
Ant Colony Optimization ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Ant Colony ◽  
Cluster Validity ◽  
Cluster Validity Index ◽  
Validity Index ◽  
Data Set

In the field of information technology, data clustering algorithms are widely used. In this paper, we proposed a new data clustering algorithm, named MADS, It is based on ant colony Optimization. MADS can automatically find clusters, depending on a few parameters that are not directly related to the data set. In addition, there are some existence technique was also utilized in our method, such as the density concept and cluster validity index (DB-index). The experiment results verified that MADS is able to discover clusters with varying shapes and is effective when applied to image segmentation.

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