A hybridized approach to data clustering

Kao, Yu-Cheng; Zahara, Erwie; Kao, I-Wei

doi:10.1016/j.eswa.2007.01.028

Cited by 286 publications

(148 citation statements)

References 13 publications

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“…In order to improve its performance further, the PSO algorithm is hybridized with K-means and N-M simplex search method. Results reveal that K-NM-PSO is superior to the K-means, PSO, and N-M simplex search method [17].…”

Section: Introductionmentioning

confidence: 93%

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Artificial Bee Colony Algorithm is More Effective on Small Size Datasets as Compared to Large Size Datasets in Data Clustering

Danish¹,

Hassan²,

Badshah³

2018

IJCA

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Data clustering is a widespread data compression, vector quantization, data analysis and data mining technique. The principle objective of data clustering is to make clusters (or groups) such that data having high degree of similarity is gathered in the same cluster while data having high degree of dissimilarity is gathered in the different clusters and plays a key role for users to organize, summarize, and steer the data adequately. In this work Artificial Bee Colony (ABC) algorithm is applied to different size datasets. Results clearly show that ABC when applied on small size datasets were more effective than those of large size datasets in terms of intra-cluster distance, computation cycles and time required to complete those cycles.

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Section: Introductionmentioning

confidence: 93%

“…The PSO which simulates bird flocking was used for clustering [17]. In order to improve its performance further, the PSO algorithm is hybridized with K-means and N-M simplex search method.…”

Section: Introductionmentioning

confidence: 99%

Artificial Bee Colony Algorithm is More Effective on Small Size Datasets as Compared to Large Size Datasets in Data Clustering

Danish¹,

Hassan²,

Badshah³

2018

IJCA

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show abstract

“…The limitations of PSO discussed in [5] can be overcome using another approach presented in [17]. Here authors have proposed a hybrid technique based on combining the k-means algorithm, Nelder-Mead (NM) simplex search, and particle swarm optimization (PSO) called K-NM-PSO.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Particle Swarm Optimization (HPSO) for Data Clustering

Mane¹,

Gaikwad²

2014

IJCA

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Data mining is the collection of different techniques. Clustering information into various cluster is one of the data mining technique. It is a method, in which each cluster must contain more similar data and have much dissimilarity between inter cluster data. Most of traditional clustering algorithms have disadvantages like initial centroid selection, local optima, low convergence rate etc. Clustering with swarm based algorithms is emerging as an alternative to more conventional clustering techniques. In this paper, a new hybrid sequential clustering approach is proposed, which uses PSO -a swarm based technique in sequence with Fuzzy k -means algorithm in data clustering. Experimentation was performed on standard dataset available online. From the result, the proposed approach helps to overcome limitations of both algorithms, improves quality of formed cluster and avoids being trapped in local optima.

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“…In addition, significant changes have been obtained in finding the response to general optimization and convergence ratio. They also combined K-means algorithm, Nelder-Mead simplex search, and particle swarm optimization, called K-NM-PSO [23]. The K-NM-PSO searches for cluster centers of an arbitrary dataset as does the KM algorithm, but it can effectively find the global optima.…”

Section: Related Workmentioning

confidence: 99%

An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

Bouyer

2016

Foundations of Computing and Decision Sciences

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Abstract. Among the data clustering algorithms, k-means (KM) algorithm is one of the most popular clustering techniques due to its simplicity and efficiency. However, k-means is sensitive to initial centers and it has the local optima problem. K-harmonic-means (KHM) clustering algorithm solves the initialization problem of k-means algorithm, but it also has local optima problem. In this paper, we develop a new algorithm for solving this problem based on an improved version of particle swarm optimization (IPSO) algorithm and KHM clustering. In the proposed algorithm, IPSO is equipped with Cuckoo Search algorithm and two new concepts used in PSO in order to improve the efficiency, fast convergence and escape from local optima. IPSO updates positions of particles based on a combination of global worst, global best with personal worst and personal best to dynamically be used in each iteration of the IPSO. The experimental result on five real-world datasets and two artificial datasets confirms that this improved version is superior to k-harmonic means and regular PSO algorithm. The results of the simulation show that the new algorithm is able to create promising solutions with fast convergence, high accuracy and correctness while markedly improving the processing time.

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A hybridized approach to data clustering

Cited by 286 publications

References 13 publications

Artificial Bee Colony Algorithm is More Effective on Small Size Datasets as Compared to Large Size Datasets in Data Clustering

Artificial Bee Colony Algorithm is More Effective on Small Size Datasets as Compared to Large Size Datasets in Data Clustering

Hybrid Particle Swarm Optimization (HPSO) for Data Clustering

An Optimized K-Harmonic Means Algorithm Combined with Modified Particle Swarm Optimization and Cuckoo Search Algorithm

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