A general stochastic clustering method for automatic cluster discovery

Tan, Swee Chuan; Ting, Kai Ming; Teng, Shyh Wei

doi:10.1016/j.patcog.2011.04.001

Cited by 18 publications

(7 citation statements)

References 27 publications

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“…However, the data to be processed are now more numerous and more complex [57,15] and the algorithms tend to mix several techniques [3] like Clarans, Birch, O-Cluster, EM or Dbscan extensions, among others. This often leads to an increased computational cost, limiting their use for large databases.…”

Section: Related Workmentioning

confidence: 99%

DIDES: a fast and effective sampling for clustering algorithm

Ros

Guillaume²

2016

Knowl Inf Syst

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As clustering algorithms become more and more sophisticated to cope with current needs, large data sets of increasing complexity, sampling is likely to provide an interesting alternative. The proposal is a distancebased algorithm: the idea is to iteratively include in the sample the furthest item from all the already selected ones. Density is managed within a post-processing step, either low or high density areas are considered. The algorithm has some nice properties: insensitive to initialization, data size and noise, it is accurate according to the Rand index and avoids many distance calculations thanks to internal optimization. Moreover it is driven by only one, meaningful, parameter, called granularity, which impacts the sample size. Compared with concurrent approaches, it proved to be as powerful as the best known methods, with the lowest CPU cost.

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Section: Related Workmentioning

confidence: 99%

DIDES: a fast and effective sampling for clustering algorithm

Ros

Guillaume²

2016

Knowl Inf Syst

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“…In K-Means, the number of clusters (k) need to be provided by a user. However, from a user point of view often it is difficult to estimate the proper number of clusters of a dataset (Chuan Tan et al, 2011;Jain, 2010). Additionally, K-Means may select poor quality initial seeds because of its random seed selection criteria.…”

Section: Motivation Behind Seed-detectivementioning

confidence: 99%

“…However, for a user it is difficult to guess and provide the correct number of clusters of a dataset (Chuan Tan et al, 2011;Jain, 2010). Additionally, because of the randomness used in the initial seed selection, K-Means may select poor quality initial seeds resulting in poor quality clusters produced from a dataset (Bagirov, 2008;Bai et al, 2011;Maitra et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

ModEx and Seed-Detective: Two novel techniques for high quality clustering by using good initial seeds in K-Means

Rahman

Islam

Bossomaier

2015

Journal of King Saud University - Computer and Information Scie

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In this paper we present two clustering techniques called ModEx and Seed-Detective. ModEx is a modified version of an existing clustering technique called Ex-Detective. It addresses some limitations of Ex-Detective. Seed-Detective is a combination of ModEx and Simple KMeans. Seed-Detective uses ModEx to produce a set of high quality initial seeds that are then given as input to K-Means for producing the final clusters. The high quality initial seeds are expected to produce high quality clusters through K-Means. The performances of Seed-Detective and ModEx are compared with the performances of Ex-Detective, PAM, Simple K-Means (SK), Basic Farthest Point Heuristic (BFPH) and New Farthest Point Heuristic (NFPH). We use three cluster evaluation criteria namely F-measure, Entropy and Purity and four natural datasets that we obtain from the UCI Machine learning repository. In the datasets our proposed techniques perform better than the existing techniques in terms of F-measure, Entropy and Purity. The sign test results suggest a statistical significance of the superiority of Seed-Detective (and ModEx) over the existing techniques. ª 2015 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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“…Based on literature (Sayed et al, 2009;Tan et al, 2011), the problem of determination of the k number of clusters have been solved using one of these two approaches; the estimation clustering approach operates by using one of clustering performance metrics. Initially such an approach defines the range for k values; low and high value, then execute the clustering method with different k clusters and measure the performance metrics.…”

Section: Introductionmentioning

confidence: 99%

“…The second approach is the swarm based approach that mimics the capability of swarm insects such as ants, flocks, bees, etc. to solve hard problems (Tan et al, 2011). Swarm based approach utilize swarm like agents to group data directly without the need to define the number of clusters.…”

Section: Introductionmentioning

confidence: 99%

Document Clustering Based on Firefly Algorithm

Mohammed¹,

Yusof²,

Husni³

2015

Journal of Computer Science

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Document clustering is widely used in Information Retrieval however, existing clustering techniques suffer from local optima problem in determining the k number of clusters. Various efforts have been put to address such drawback and this includes the utilization of swarm-based algorithms such as particle swarm optimization and Ant Colony Optimization. This study explores the adaptation of another swarm algorithm which is the Firefly Algorithm (FA) in text clustering. We present two variants of FA; Weight-based Firefly Algorithm (WFA) and Weight-based Firefly Algorithm II (WFA II ). The difference between the two algorithms is that the WFA II, includes a more restricted condition in determining members of a cluster. The proposed FA methods are later evaluated using the 20Newsgroups dataset. Experimental results on the quality of clustering between the two FA variants are presented and are later compared against the one produced by particle swarm optimization, K-means and the hybrid of FA and -K-means. The obtained results demonstrated that the WFA II outperformed the WFA, PSO, K-means and FA-Kmeans. This result indicates that a better clustering can be obtained once the exploitation of a search solution is improved.

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A general stochastic clustering method for automatic cluster discovery

Cited by 18 publications

References 27 publications

DIDES: a fast and effective sampling for clustering algorithm

DIDES: a fast and effective sampling for clustering algorithm

ModEx and Seed-Detective: Two novel techniques for high quality clustering by using good initial seeds in K-Means

Document Clustering Based on Firefly Algorithm

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