The K-means clustering algorithm based on density and ant colony

Peng, Yuqing; Hou, Xiangdan; Shang, Lei

doi:10.1109/icnnsp.2003.1279307

Cited by 19 publications

(11 citation statements)

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“…There are some works related to the algorithms of clusters based on ACO. Yuqing et al Proposes algorithm of K-means clusters based on density and on the Colony of Ants [13]. This algorithm is a new K-means algorithm based on the density and theory of ants, which solved the problem of the local minimum by the random ants, besides manipulating the initial parameters of K-means.…”

Section: Ant Colony Optimizationmentioning

confidence: 98%

A Two Stage Clustering Method Combining Self-Organizing Maps and Ant K-Means

Souza

Ludermir

Almeida

2009

Artificial Neural Networks – ICANN 2009

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Abstract. This paper proposes a clustering method SOMAK, which is composed by Self-Organizing Maps (SOM) followed by the Ant K-means (AK) algorithm. SOM is an Artificial Neural Network (ANN), which has one of its characteristics, the nonlinear projection from a high dimensionality of the sensorial space. AK is based in the Ant Colony Optimization (ACO), which is a recently proposed meta-heuristic approach for solving hard combinatorial optimization problems. The AK algorithm modifies the K-means on locating the objects and these are then clustered according to the probabilities which in turn are updated by the pheromone. The SOMAK has a good performance when compared with some clustering techniques and reduces the computational time.

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Section: Ant Colony Optimizationmentioning

confidence: 98%

A Two Stage Clustering Method Combining Self-Organizing Maps and Ant K-Means

Souza

Ludermir

Almeida

2009

Artificial Neural Networks – ICANN 2009

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“…Handl et al (2003a,b) presented a comparative study of the performance of ant-based clustering algorithm against some classical clustering algorithms. Yuqing et al (2003) proposed a K-means clustering algorithm based on ant colony. Tsai et al (2004) presented an efficient clustering approach for large databases.…”

Section: Ant Colony Optimization For Clusteringmentioning

confidence: 99%

A survey: hybrid evolutionary algorithms for cluster analysis

Hasan¹,

Ramakrishnan²

2011

Artif Intell Rev

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Clustering is a popular data analysis and data mining technique. It is the unsupervised classification of patterns into groups. Many algorithms for large data sets have been proposed in the literature using different techniques. However, conventional algorithms have some shortcomings such as slowness of the convergence, sensitive to initial value and preset classed in large scale data set etc. and they still require much investigation to improve performance and efficiency. Over the last decade, clustering with ant-based and swarm-based algorithms are emerging as an alternative to more traditional clustering techniques. Many complex optimization problems still exist, and it is often very difficult to obtain the desired result with one of these algorithms alone. Thus, robust and flexible techniques of optimization are needed to generate good results for clustering data. Some algorithms that imitate certain natural principles, known as evolutionary algorithms have been used in a wide variety of real-world applications. Recently, much research has been proposed using hybrid evolutionary algorithms to solve the clustering problem. This paper provides a survey of hybrid evolutionary algorithms for cluster analysis.

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“…Yuqing et al [2] used ACO to improve the K-Means algorithm. In this algorithm, the ants move objects in the 2D board frequently according to similarity.…”

Section: Related Workmentioning

confidence: 99%

Clustering categorical data using a swarm-based method

Izakian

Abraham

Snášel

2009

2009 World Congress on Nature &Amp; Biologically Inspired Computing (NaBIC)

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Abstract-The K-Modes algorithm is one of the most popular clustering algorithms in dealing with categorical data. But the random selection of starting centers in this algorithm may lead to different clustering results and falling into local optima. In this paper we proposed a swarm-based K-Modes algorithm. The experimental results over two well known Soybean and Congressional voting categorical data sets show that our method can find the optimal global solutions and can make up the K-Modes shortcoming.

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The K-means clustering algorithm based on density and ant colony

Cited by 19 publications

References 0 publications

A Two Stage Clustering Method Combining Self-Organizing Maps and Ant K-Means

A Two Stage Clustering Method Combining Self-Organizing Maps and Ant K-Means

A survey: hybrid evolutionary algorithms for cluster analysis

Clustering categorical data using a swarm-based method

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