Add to ORKGSummarization: This paper presents a new stochastic nature inspired methodology, which is based on the concepts of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), for optimally clustering N objects into K clusters. Due to the nature of stochastic and population-based search, the proposed algorithm can overcome the drawbacks of traditional clustering methods. Its performance is compared with other popular stochastic/metaheuristic methods like genetic algorithm and Tabu search. The proposed algorithm has been implemented and tested on several datasets with very good results.Παρουσιάστηκε στο: 22nd European Conference on Operational Researc
[[abstract]]Clustering analysis aims at discovering groups and identifying interesting distributions...
Summarization: This paper presents a new hybrid algorithm, which is based on the concepts of Particl...
Clustering techniques have received attention in many areas including engineering, medicine, biology...
Summarization: This paper presents a new stochastic nature inspired methodology, which is based on t...
Summarization: Clustering is a very important problem that has been addressed in many contexts and b...
Summarization: This paper presents a new stochastic methodology, which is based on the concepts of g...
Summarization: This paper presents a new memetic algorithm, which is based on the concepts of geneti...
Summarization: This paper presents a new memetic algorithm, which is based on the concepts of Geneti...
Summarization: Cluster analysis is an important tool for data exploration and it has been applied in...
The clustering problem has been studied by many researchers using various approaches, including tabu...
Finding clusters in data is a challenging problem. Given a dataset, we usually do not know the numbe...
Clustering analysis includes a number of different algorithms and methods for grouping objects by th...
The clustering algorithms have evolved over the last decade. With the continuous success of natural ...
This paper presents an ant colony optimization methodology for optimally clustering N objects into K...
Abstract—In this paper, the proposed approach is an unique combination of two most popular clusterin...
[[abstract]]Clustering analysis aims at discovering groups and identifying interesting distributions...
Summarization: This paper presents a new hybrid algorithm, which is based on the concepts of Particl...
Clustering techniques have received attention in many areas including engineering, medicine, biology...
Summarization: This paper presents a new stochastic nature inspired methodology, which is based on t...
Summarization: Clustering is a very important problem that has been addressed in many contexts and b...
Summarization: This paper presents a new stochastic methodology, which is based on the concepts of g...
Summarization: This paper presents a new memetic algorithm, which is based on the concepts of geneti...
Summarization: This paper presents a new memetic algorithm, which is based on the concepts of Geneti...
Summarization: Cluster analysis is an important tool for data exploration and it has been applied in...
The clustering problem has been studied by many researchers using various approaches, including tabu...
Finding clusters in data is a challenging problem. Given a dataset, we usually do not know the numbe...
Clustering analysis includes a number of different algorithms and methods for grouping objects by th...
The clustering algorithms have evolved over the last decade. With the continuous success of natural ...
This paper presents an ant colony optimization methodology for optimally clustering N objects into K...
Abstract—In this paper, the proposed approach is an unique combination of two most popular clusterin...
[[abstract]]Clustering analysis aims at discovering groups and identifying interesting distributions...
Summarization: This paper presents a new hybrid algorithm, which is based on the concepts of Particl...
Clustering techniques have received attention in many areas including engineering, medicine, biology...