A new hybrid approach for data clustering

Yazdani, Danial; Golyari, Sara; Meybodi, Mohammad Reza

doi:10.1109/istel.2010.5734153

Cited by 13 publications

(2 citation statements)

References 17 publications

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“…In [18], the author clusters using PSO and K-means. Additionally, A hybrid dynamic data clustering technique called KCPSO, which combines k-means with combinatorial PSO, was developed by [22]. The proposed KCPSO does not require a specific number of clusters throughout the clustering process.…”

Section: Literature Surveymentioning

confidence: 99%

Attribute Selection Algorithm with Clustering based Optimization Approach based on Mean and Similarity Distance

Kaseebhotla,

Rao,

Rao

2023

IJRITCC

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With hundreds or thousands of attributes in high-dimensional data, the computational workload is challenging. Attributes that have no meaningful influence on class predictions throughout the classification process increase the computing load. This article's goal is to use attribute selection to reduce the size of high-dimensional data, which will lessen the computational load. Considering selected attribute subsets that cover all attributes. As a result, there are two stages to the process: filtering out superfluous information and settling on a single attribute to stand in for a group of similar but otherwise meaningless characteristics. Numerous studies on attribute selection, including backward and forward selection, have been undertaken. This experiment and the accuracy of the categorization result recommend a k-means based PSO clustering-based attribute selection. It is likely that related attributes are present in the same cluster while irrelevant attributes are not identified in any clusters. Datasets for Credit Approval, Ionosphere, Annealing, Madelon, Isolet, and Multiple Attributes are employed alongside two other high-dimensional datasets. Both databases include the class label for each data point. Our test demonstrates that attribute selection using k-means clustering may be done to offer a subset of characteristics and that doing so produces classification outcomes that are more accurate than 80%.

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Section: Literature Surveymentioning

confidence: 99%

Attribute Selection Algorithm with Clustering based Optimization Approach based on Mean and Similarity Distance

Kaseebhotla,

Rao,

Rao

2023

IJRITCC

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“…According to this property, AFSA model was proposed by Free-Movement, Food-Search, Swarm-Movement and Follow Behaviors in order to search the problem space. This algorithm is used in different applications [6] such as neural networks [7,8], color quantization [9], dynamic optimization problems [10], physics [11], global optimization [12][13][14][15], and data clustering [16].…”

mentioning

confidence: 99%

Fault-Tolerant Routing Scheme Based on Leach for Wireless Sensor Networks

Hellel¹,

Lehsaini²,

Guyennet³

2015

IJCNC

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Clustering problems are considered amongst the prominent challenges in statistics and computational science. Clustering of nodes in wireless sensor networks which is used to prolong the lifetime of networks is one of the difficult tasks of clustering procedure. In order to perform nodes' clustering, a number of nodes are determined as cluster heads and other ones are joined to one of these heads, based on different criteria e.g. Euclidean distance. So far, different approaches have been proposed for this process, where swarm and evolutionary algorithms contribute in this regard. In this study, a novel algorithm is proposed based on Artificial Fish Swarm Algorithm (AFSA) for clustering procedure. In the proposed method, the performance of the standard AFSA is improved by increasing balance between local and global searches. Furthermore, a new mechanism has been added to the base algorithm for improving convergence speed in clustering problems. Performance of the proposed technique is compared to a number of state-of-the-art techniques in this field and the outcomes indicate the supremacy of the proposed technique.

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Network Log Clustering Using K-Means Algorithm

Sharma¹,

Parvat²

2012

Lecture Notes in Electrical Engineering

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A new hybrid approach for data clustering

Cited by 13 publications

References 17 publications

Attribute Selection Algorithm with Clustering based Optimization Approach based on Mean and Similarity Distance

Attribute Selection Algorithm with Clustering based Optimization Approach based on Mean and Similarity Distance

Fault-Tolerant Routing Scheme Based on Leach for Wireless Sensor Networks

Network Log Clustering Using K-Means Algorithm

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