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

Chaimontree, Santhana; Atkinson, Katie; Coenen, Frans

doi:10.1007/978-3-642-27609-5_3

Cited by 20 publications

(15 citation statements)

References 22 publications

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“…This framework implements mutual collaboration, and it requires human interference to determine the suitability of the collaborated information. MAS-based clustering framework that can improve the initial cluster centers at each agent was also proposed recently [25]. Results of the proposed collaborative clustering showed an improvement over noncollaborative agent-based clustering.…”

Section: Related Workmentioning

confidence: 99%

Multi-Agent System Combined With Distributed Data Mining for Mutual Collaboration Classification

et al. 2021

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Distributed Data Mining (DDM) has been proposed as a means to deal with the analysis of distributed data, where DDM discovers patterns and implements prediction based on multiple distributed data sources. However, DDM faces several problems in terms of autonomy, privacy, performance and implementation. DDM requires homogeneity regarding environment, control, administration and the classification algorithm(s), and such that requirements are too strict and inflexible in many applications. In this paper, we propose the employment of a Multi-Agent System (MAS) to be combined with DDM (MAS-DDM). MAS is a mechanism for creating goal-oriented autonomous agents within shared environments with communication and coordination facilities. We shall show that MAS-DDM is both desirable and beneficial. In MAS-DDM, agents could communicate their beliefs (calculated classification) by covering private and non-sharable data, and other agents decide whether the use of such beliefs in classifying instances and adjusting their prior assumptions about each class of data. In MAS-DDM, we will develop and use a modified Naive Bayesian algorithm because (1) Naive Bayesian has been shown to be the most used algorithm to deal with uncertain data, and (2) to show that even if all agents in MAS-DDM use the same algorithm, MAS-DDM preforms better than DDM approaches with non-communicating processes. Point (2) provide an evidence that the exchange of information between agents helps in increasing the accuracy of the classification task significantly.

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Section: Related Workmentioning

confidence: 99%

Multi-Agent System Combined With Distributed Data Mining for Mutual Collaboration Classification

et al. 2021

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“…is framework implements mutual collaboration, and it requires human interference to determine the suitability of the collaborated information. MAS-based clustering framework that can improve the initial cluster centers at each agent was also proposed recently [31].…”

Section: Related Workmentioning

confidence: 99%

“…is framework also maintains the particularity of each agent and allows information sharing among local agents to enhance the capabilities of autonomous agents and cover private and nonsharable data. In this research, as similar to the mutual collaborative clustering proposed in [31], an approach of mutual collaborative classification is proposed.…”

Section: Related Workmentioning

confidence: 99%

Multiagent System for Mutual Collaboration Classification for Cancer Detection

Qasem

Hudaib

Obeid

2019

Mathematical Problems in Engineering

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A multiagent system (MAS) is a mechanism for creating goal-oriented autonomous agents in shared environments with communication and coordination facilities. Distributed data mining benefits from this goal-oriented mechanism by implementing various distributed clustering, classification, and prediction techniques. Hence, this study developed a novel multiagent model for distributed classification tasks in cancer detection with the collaboration of several hospitals worldwide using different classifier algorithms. A hospital agent requests help from other agents for instances that are difficult to classify locally. The agents communicate their beliefs (calculated classification), and others decide on the benefit of using such beliefs in classifying instances and adjusting their prior assumptions on each class of data. A MAS model state and behavior and communication are then developed to facilitate information sharing among agents. Regarding accuracy, implementing the proposed approach in comparison with typically different noncommunicated distributed classifications shows that sharable information considerably increases the classification task accuracy by 25.77%.

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“…Step 3: Move all data points into the clusters by making use of the Euclidean's distance equation (1). Now recalculate all the new centroids.…”

Section: Content Andusage Miningmentioning

confidence: 99%

“…The MAS offers another alternative to handle data of large quantities by means of harnessing the numbers of processors and their power and this has the additional advantage of control not being centralized. This makes all these systems more versatile and robust [1].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Agent Bio- Inspired System to Map Learners with Learning Resources using Clustering Based Personalization

Gottipati¹,

Prasath²

2019

IJITEE

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The work makes an examination of clustering methods in a multi-agent system which is fully decentralized. This has the goal of grouping agents that have similar data or objectives as in the case of traditional clustering. But, this adds to some more additional constraints wherein the agent will have to be in the same place as opposed to being collected within a centralized database. For doing this, it will connect to agents within a random network and will search for them in a peer-to-peer based fashion for the other agents that are similar. The primary aim here was to tackle the basic problem in clustering on the Internet scale thus creating methods where the agents may be grouped thus forming coalitions. For the purpose of investigating the decentralized approaches and their feasibility, the work presents the K-means clustering, the multi-agent Firefly Algorithm, (FA) and the Differential Evolution (DE). This is done for a reasonable number to times and will be surprisingly good. The results of the experiment prove that the multi-agent firefly clustering has better performance compared to that of a multi-agent K-Means clustering or a multi-agent DE clustering.

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

Cited by 20 publications

References 22 publications

Multi-Agent System Combined With Distributed Data Mining for Mutual Collaboration Classification

Multi-Agent System Combined With Distributed Data Mining for Mutual Collaboration Classification

Multiagent System for Mutual Collaboration Classification for Cancer Detection

A Multi-Agent Bio- Inspired System to Map Learners with Learning Resources using Clustering Based Personalization

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