On Improving Clustering in Numerical Databases with Artificial Ants

Monmarché, Nicolas; Slimane, Mohamed; Venturini, Gilles

doi:10.1007/3-540-48304-7_83

Cited by 73 publications

(55 citation statements)

References 1 publication

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“…In his work, it is claimed that this complexity seeking attribute increases the effectiveness of sorting and the clustering process. Monmarché et al propose a hybridisation of the SACA by including the K-Means, called the AntClass algorithm [Monmarché, 1999], [Monmarché et al, 1999b]. The approach differs from the SACA since the algorithm allows an ant to drop more than one object in the same cell, forming heaps of objects.…”

Section: Standard Ant Clustering Algorithm (Saca)mentioning

confidence: 99%

A Multiple Pheromone Ant Clustering Algorithm

Chircop

Buckingham

2014

Nature Inspired Cooperative Strategies for Optimization (NICSO 2013)

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Section: Standard Ant Clustering Algorithm (Saca)mentioning

confidence: 99%

A Multiple Pheromone Ant Clustering Algorithm

Chircop

Buckingham

2014

Nature Inspired Cooperative Strategies for Optimization (NICSO 2013)

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“…Monmarche [59] [60] combined the stochastic and exploratory principles of clustering ants with the deterministic and heuristic of the popular k-means algorithm in order to improve the convergence of the ant-based clustering algorithm. The proposed hybrid method is called AntClass and is based on the work of Lumer and Faieta [50].…”

Section: Ant-based Clustering In the Literaturementioning

confidence: 99%

Ant-based Clustering Algorithms: A Brief Survey

Jafar¹,

Sivakumar²

2010

IJCTE

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Abstract-Ant-based clustering is a biologically inspired data clustering technique. Clustering task aims at the unsupervised classification of patterns in different groups. Clustering problem has been approached from different disciplines during last years. In recent years, many algorithms have been developed for solving numerical and combinatorial optimization problems. Most promising among them are swarm intelligence algorithms. Clustering with swarm-based algorithms is emerging as an alternative to more conventional clustering techniques. These algorithms have recently been shown to produce good results in a wide variety of real-world applications. During the last five years, research on and with the ant-based clustering algorithms has reached a very promising state. In this paper, a brief survey on ant-based clustering algorithms is described. We also present some applications of ant-based clustering algorithms.

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“…It seems that some feedback mechanism (using local density or similarity of data items) determines the probability that an ant will pick up or drop a corpse. Such behavior is used as a model to design several algorithms for clustering data [6,7,8,9,10,11]. Besides nest cleaning, many functions of aggregation behavior have been observed in ants and ant like agents [12,13,14].…”

Section: Introductionmentioning

confidence: 99%

Aggregation Pheromone Density Based Image Segmentation

Ghosh

Kothari

Ghosh

2006

Computer Vision, Graphics and Image Processing

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Abstract. Ants, bees and other social insects deposit pheromone (a type of chemical) in order to communicate between the members of their community. Pheromone that causes clumping or clustering behavior in a species and brings individuals into a closer proximity is called aggregation pheromone. This paper presents a novel method for image segmentation considering the aggregation behavior of ants. Image segmentation is viewed as a clustering problem which aims to partition a given set of pixels into a number of homogenous clusters/segments. At each location of data point representing a pixel an ant is placed; and the ants are allowed to move in the search space to find out the points with higher pheromone density. The movement of an ant is governed by the amount of pheromone deposited at different points of the search space. More the deposited pheromone, more is the aggregation of ants. This leads to the formation of homogenous groups of data. The proposed algorithm is evaluated on a number of images using different cluster validity measures. Results are compared with those obtained using average linkage and k-means clustering algorithms and are found to be better.

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On Improving Clustering in Numerical Databases with Artificial Ants

Cited by 73 publications

References 1 publication

A Multiple Pheromone Ant Clustering Algorithm

A Multiple Pheromone Ant Clustering Algorithm

Ant-based Clustering Algorithms: A Brief Survey

Aggregation Pheromone Density Based Image Segmentation

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