An efficient clustering approach using ant colony algorithm in mutidimensional search space

Jiang, Lei; Ding, Lixin; Peng, Yang; Zhao, Chen

doi:10.1109/fskd.2011.6019741

Cited by 6 publications

(4 citation statements)

References 16 publications

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“…This cooperative behavior ends up by establishing optimized shortest route path [43]. A dedicated formulation with respect to other applications of Ant Colony clustering is used to implement Ant Colony clustering approach [20], [43], [44]. The implementation is carried out in three steps; initialization, first iteration, and successive iterations.…”

Section: B Ant Colony Swarm Clusteringmentioning

confidence: 99%

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Clustering of Wind Power Patterns Based on Partitional and Swarm Algorithms

Munshi

2020

IEEE Access

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Section: B Ant Colony Swarm Clusteringmentioning

confidence: 99%

“…Fig. 2 represents the pseudo-code of the biased roulette wheel [44]. The average of data points assigned to same cluster helps in obtaining the set of centroids Ca(1) for each Sa(1) vector.…”

Section: ) Initializationmentioning

confidence: 99%

Clustering of Wind Power Patterns Based on Partitional and Swarm Algorithms

Munshi

2020

IEEE Access

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“…There are various legitimacy functions used like cosine similarity, Euclidean, Manhattan etc. Clustering aims to group data based on the theory of minimum intra-group divergence and maximum inter-group divergence (Jiang et al, 2011;Xing et al, 2016).…”

Section: Background and Overview Of K-means And Aco Algorithm'smentioning

confidence: 99%

ACOCA: Ant Colony Optimization Based Clustering Algorithm for Big Data Preprocessing

Singh¹,

Singh²,

Pant³

2019

Int J Math, Eng, Manag Sci

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Big Data is rapidly gaining impetus and is attracting a community of researchers and organization from varying sectors due to its tremendous potential. Big Data is considered as a prospective raw material to acquire domain specific knowledge to gain insights related to management, planning, forecasting and security etc. Due to its inherent characteristics like capacity, swiftness, genuineness and diversity Big Data hampers the efficiency and effectiveness of search and leads to optimization problems. In this paper we explore the complexity imposed by big search spaces leading to optimization issues. In order to overcome the above mentioned issues we propose a hybrid algorithm for Big Data preprocessing ACO-clustering algorithm approach. The proposed algorithm can help to increase search speed by optimizing the process. As the proposed method using ant colony optimization with clustering algorithm it will also contribute to reducing pre-processing time and increasing analytical accuracy and efficiency.

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“…They use Manhattan distance as the fitness function and consider the pheromone only when the ants select paths. Jiang et al [29] split the structure of solutions into two parts. The first part is used to decide which patterns will be included in the following computation.…”

Section: Ant Colony Optimization Formentioning

confidence: 99%

A Multiple Pheromone Table Based Ant Colony Optimization for Clustering

Tsai

Chiang

et al. 2015

Mathematical Problems in Engineering

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Ant colony optimization (ACO) is an efficient heuristic algorithm for combinatorial optimization problems, such as clustering. Because the search strategy of ACO is similar to those of other well-known heuristics, the probability of searching particular regions will be increased if better results are found and kept. Although this kind of search strategy may find a better approximate solution, it also has a high probability of losing the potential search directions. To prevent the ACO from losing too many potential search directions at the early iterations, a novel pheromone updating strategy is presented in this paper. In addition to the “original” pheromone table used to keep track of thepromisinginformation, a second pheromone table is added to the proposed algorithm to keep track of theunpromisinginformation so as to increase the probability of searching directions worse than the current solutions. Several well-known clustering datasets are used to evaluate the performance of the proposed method in this paper. The experimental results show that the proposed method can provide better results than ACO and other clustering algorithms in terms of quality.

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An efficient clustering approach using ant colony algorithm in mutidimensional search space

Cited by 6 publications

References 16 publications

Clustering of Wind Power Patterns Based on Partitional and Swarm Algorithms

Clustering of Wind Power Patterns Based on Partitional and Swarm Algorithms

ACOCA: Ant Colony Optimization Based Clustering Algorithm for Big Data Preprocessing

A Multiple Pheromone Table Based Ant Colony Optimization for Clustering

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