Best Clustering Configuration Metrics: Towards Multiagent Based Clustering

Chaimontree, Santhana; Atkinson, Katie; Coenen, Frans

doi:10.1007/978-3-642-17316-5_5

Cited by 22 publications

(20 citation statements)

References 19 publications

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“…Note that the chosen value of t can significantly affect the number of Clustering Agents that are spawned. The authors proposed a method, in the context of MABC, to identify the most appropriate value for t in [10] where they reported on experiments conducted with respect to a number of cluster validation techniques to identify the optimum parameters for clustering algorithms. In [10] the desired final configuration was generated using a sequence of parameter values to produce a collection of cluster configurations from which the most appropriate was selected.…”

Section: Biding Phase Founded On the K-nn Paradigmmentioning

confidence: 99%

“…The authors proposed a method, in the context of MABC, to identify the most appropriate value for t in [10] where they reported on experiments conducted with respect to a number of cluster validation techniques to identify the optimum parameters for clustering algorithms. In [10] the desired final configuration was generated using a sequence of parameter values to produce a collection of cluster configurations from which the most appropriate was selected. These reported experiments clearly demonstrated that there was no single "best" value for t. From the experiments it was also clear there were many factors that influence the choice of the best value for t, such as the nature of the data set size, distribution of values, number of potential clusters and so on; consequently it was not possible to identify specific correlations between t values and the nature of the data set.…”

Section: Biding Phase Founded On the K-nn Paradigmmentioning

confidence: 99%

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A framework for Multi-Agent Based Clustering

Chaimontree

Atkinson

Coenen

2011

Auton Agent Multi-Agent Syst

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A framework to support Multi-Agent Based Clustering (MABC) is described. A unique feature of the framework is that it provides mechanisms to allow agents to negotiate so as to improve an initial cluster configuration. The framework encourages a two phase approach to clustering. During the first phase clustering agents bid for records in the input data and form an initial cluster configuration. In the second phase (the negotiation phase) agents pass individual records to each other so as to improve the initial configuration. The communication framework and its operation is fully described in terms of the performatives used and from an algorithmic perspective. The reported evaluation was conducted using benchmark data sets. The results demonstrate that the supported agent negotiation produces enhanced clustering results.

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Section: Biding Phase Founded On the K-nn Paradigmmentioning

confidence: 99%

Section: Biding Phase Founded On the K-nn Paradigmmentioning

confidence: 99%

A framework for Multi-Agent Based Clustering

Chaimontree

Atkinson

Coenen

2011

Auton Agent Multi-Agent Syst

Self Cite

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“…Sum of squared error (SSE) is calculated by summing the squared distance between the cluster centroid (Cen i ) and every object within the cluster [7]. To determine the best cluster configuration we try to minimize SSE.…”

Section: Sum Of Squared Error(sse)mentioning

confidence: 99%

Feature Selection using Genetic Algorithm for Clustering high Dimensional Data

Kouser¹,

Priyam²

2018

IJET

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One of the open problems of modern data mining is clustering high dimensional data. For this in the paper a new technique called GAHDClustering is proposed, which works in two steps. First a GA-based feature selection algorithm is designed to determine the optimal feature subset; an optimal feature subset is consisting of important features of the entire data set next, a K-means algorithm is applied using the optimal feature subset to find the clusters. On the other hand, traditional K-means algorithm is applied on the full dimensional feature space. Finally, the result of GA-HDClustering is compared with the traditional clustering algorithm. For comparison different validity matrices such as Sum of squared error (SSE), Within Group average distance (WGAD), Between group distance (BGD), Davies-Bouldin index(DBI), are used .The GA-HDClustering uses genetic algorithm for searching an effective feature subspace in a large feature space. This large feature space is made of all dimensions of the data set. The experiment performed on the standard data set revealed that the GA-HDClustering is superior to traditional clustering algorithm.

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“…Note that the chosen value of t can significantly affect the number of Clustering Agents that are spawned. The authors proposed a method to identify the most appropriate value for t in [5].…”

Section: Biding Phase Founded On the Knn Spawning Strategymentioning

confidence: 99%

A Multi-agent Based Approach to Clustering: Harnessing the Power of Agents

Chaimontree

Atkinson

Coenen

2012

Lecture Notes in Computer Science

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Abstract. A framework for multi-agent based clustering is described whereby individual agents represent individual clusters. A particular feature of the framework is that, after an initial cluster configuration has been generated, the agents are able to negotiate with a view to improving on this initial clustering. The framework can be used in the context of a number of clustering paradigms, two are investigated: K-means and KNN. The reported evaluation demonstrates that negotiation can serve to improve on an initial cluster configuration.

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Best Clustering Configuration Metrics: Towards Multiagent Based Clustering

Cited by 22 publications

References 19 publications

A framework for Multi-Agent Based Clustering

A framework for Multi-Agent Based Clustering

Feature Selection using Genetic Algorithm for Clustering high Dimensional Data

A Multi-agent Based Approach to Clustering: Harnessing the Power of Agents

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