Agent-Based Distributed Data Mining: The KDEC Scheme

Klusch, Matthias; Lodi, Stefano; Moro, Gianluca

doi:10.1007/3-540-36561-3_5

Cited by 67 publications

(33 citation statements)

References 16 publications

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“…PADMA is therefore based on a centralised architecture, whereas PAPYRUS adopts a Peer-To-Peer model where both data and results can be moved between agents according to given MAS strategies. Another MAC based on the Peer-To-Peer model is proposed in [22] where a distributed density-based clustering algorithm, called KDEC [15], is used. Density estimation samples are transmitted, instead of data values, outside the site of origin in order to preserve data privacy.…”

Section: Previous Workmentioning

confidence: 99%

Best Clustering Configuration Metrics: Towards Multiagent Based Clustering

Chaimontree

Atkinson

Coenen

2010

Lecture Notes in Computer Science

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Abstract. Multi-Agent Clustering (MAC) requires a mechanism for identifying the most appropriate cluster configuration. This paper reports on experiments conducted with respect to a number of validation metrics to identify the most effective metric with respect to this context. This paper also describes a process whereby such metrics can be used to determine the optimum parameters typically required by clustering algorithms, and a process for incorporating this into a MAC framework to generate best cluster configurations with minimum input from end users.

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Section: Previous Workmentioning

confidence: 99%

Best Clustering Configuration Metrics: Towards Multiagent Based Clustering

Chaimontree

Atkinson

Coenen

2010

Lecture Notes in Computer Science

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“…This would involve autonomously operating units of KDD software interacting with one another by requesting and providing services; thus, for example, a technology actor engaging in an analysis interaction with a human actor could enlist the help of another technology actor capable of offering special expertise relevant to the problem at hand. To a certain extent, this is already happening in various schemes for agent-based KDD such as those mentioned in (Klusch et al, 2003), but so far, the role of the agents has been subject to the same limitations as the role of technology in the KDD process in general, the agent-based approach mainly serving as a strategy for distributing computations.…”

Section: Discussionmentioning

confidence: 99%

A Conceptual Model of Actors and Interactions for the Knowledge Discovery Process

Tuovinen

2016

Proceedings of the 8th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

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Abstract:The knowledge discovery process is traditionally viewed as a sequence of operations to be applied to data; the human aspect of the process is seldom taken into account, and when it is, it is mainly the roles and actions of domain and technology experts that are considered. However, non-experts can also play an important role in knowledge discovery, and furthermore, the role of technology in the process may also be substantially expanded from what it traditionally has been, with special software facilitating interactions among human actors and even operating as an actor in its own right. This diversification of the knowledge discovery process is helpful in finding tenable solutions to the new problems presented by the current deluge of digital data, but only if the process model used to manage the process adequately represents the variety of forms that the process can take. The paper addresses this requirement by presenting a conceptual model that can be used to describe different types of knowledge discovery processes in terms of the actors involved and the interactions they have with one another. Additionally, the paper discusses how the interactions can be facilitated to provide effective support for each different type of process. As a future perspective, the paper considers the implications of intelligent software taking on responsibilities traditionally reserved for human actors.

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“…A more recent MAS approach to clustering is the KDEC scheme proposed by Klusch et al [8]. KDEC is a distributed density-based clustering algorithm.…”

Section: Previous Workmentioning

confidence: 99%

Clustering in a Multi-Agent Data Mining Environment

Chaimontree

Atkinson

Coenen

2010

Lecture Notes in Computer Science

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Abstract. A Multi-Agent based approach to clustering using a generic Multi-Agent Data Mining (MADM) framework is described. The process use a collection of agents, running several different clustering algorithms, to determine a "best" cluster configuration. The issue of determining the most appropriate configuration is a challenging one, and is addressed in this paper by considering two metrics, total Within Group Average Distance (WGAD) to determine cluster cohesion, and total Between Group Distance (BGD) to determine separation. The proposed process is implemented using the MASminer MADM framework which is also introduced in this paper. Both the clustering technique and MASminer are evaluated. Comparison of the two "best fit" measures indicates that WGAD can be argued to be the most appropriate metric.

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Agent-Based Distributed Data Mining: The KDEC Scheme

Cited by 67 publications

References 16 publications

Best Clustering Configuration Metrics: Towards Multiagent Based Clustering

Best Clustering Configuration Metrics: Towards Multiagent Based Clustering

A Conceptual Model of Actors and Interactions for the Knowledge Discovery Process

Clustering in a Multi-Agent Data Mining Environment

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