Open Access
An ACO-Based Clustering Algorithm
Yucheng Kao,Kevin Cheng +1 more
- pp 340-347
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TLDR
Ant Colony Optimization for Clustering (ACOCO) as mentioned in this paper uses both accumulated pheromone and the heuristic information, the distances between data objects and cluster centers of ants, to guide artificial ants to group data objects into proper clusters.Abstract:
Data clustering is one of important research topics of data mining. In this paper, we propose a new clustering algorithm based on ant colony optimization, called Ant Colony Optimization for Clustering (ACOC). At the core of the algorithm we use both the accumulated pheromone and the heuristic information, the distances between data objects and cluster centers of ants, to guide artificial ants to group data objects into proper clusters. This allows the algorithm to perform the clustering process more effectively and efficiently. Due to the nature of stochastic and population-based search, the ACOC can overcome the drawbacks of traditional clustering methods that easily converge to local optima. Experimental results show that the ACOC can find relatively good solutions.read more
Citations
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A novel clustering approach: Artificial Bee Colony (ABC) algorithm
Dervis Karaboga,Celal Ozturk +1 more
TL;DR: Simulation results indicate that ABC algorithm can efficiently be used for multivariate data clustering and is compared with Particle Swarm Optimization (PSO) algorithm and other nine classification techniques from the literature.
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Clustering using firefly algorithm: Performance study
TL;DR: It is concluded that the FA can be efficiently used for clustering and compared with other two nature inspired techniques — Artificial Bee Colony, Particle Swarm Optimization and other nine methods used in the literature.
Journal ArticleDOI
A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm
TL;DR: An extended ABC algorithm is presented, namely, the Cooperative Article Bee Colony (CABC), which significantly improves the original ABC in solving complex optimization problems and is compared with PSO, CPSO, and ABC algorithms on clustering problems.
Journal ArticleDOI
Grey Wolf Optimizer Based on Powell Local Optimization Method for Clustering Analysis
Sen Zhang,Yongquan Zhou +1 more
TL;DR: An extended GWO algorithm based on Powell local optimization method is presented, and it significantly improves the original GWO in solving complex optimization problems and demonstrates the superior performance of PGWO algorithm.
Journal ArticleDOI
Flower Pollination Algorithm with Bee Pollinator for Cluster Analysis
TL;DR: The experiment results show that Flower Pollination Algorithm with Bee Pollinator has not only higher accuracy but also higher level of stability and the faster convergence speed can also be validated by statistical results.
References
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Book
Data Mining: Concepts and Techniques
TL;DR: This book presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects, and provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data.
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Ant system: optimization by a colony of cooperating agents
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Ant colony system: a cooperative learning approach to the traveling salesman problem
TL;DR: The results show that the ACS outperforms other nature-inspired algorithms such as simulated annealing and evolutionary computation, and it is concluded comparing ACS-3-opt, a version of the ACS augmented with a local search procedure, to some of the best performing algorithms for symmetric and asymmetric TSPs.
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Genetic algorithm-based clustering technique
TL;DR: The superiority of the GA-clustering algorithm over the commonly used K-means algorithm is extensively demonstrated for four artificial and three real-life data sets.