Kamal Ali Albashiri

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In this paper we describe EMADS, an Extendible Multi-Agent Data mining System. The EMADS vision is that of a community of data mining agents, contributed by many individuals , interacting under decentralised control to address data mining requests. EMADS is seen both as an end user application and a research tool. This paper details the EMADS vision, the(More)
In this paper we describe the concept of Meta ARM in the context of its objectives and challenges and go on to describe and analyse a number of potential solutions. Meta ARM is defined as the process of combining the results of a number of individually obtained Associate Rule Mining (ARM) operations to produce a composite result. The typical scenario where(More)
In this paper we: introduce EMADS, the Extendible Multi-Agent Data mining System, to support the dynamic creation of communities of data mining agents; explore the capabilities of such agents and demonstrate (by experiment) their application to data mining on distributed data. Although, EMADS is not restricted to one data mining task, the study described(More)
A generic and extendible Multi-Agent Data Mining (MADM) framework, EMADS (the Extendible Multi-Agent Data mining System) is described. The central feature of the framework is that it avoids the use of agreed metalanguage formats by supporting a system of wrappers. The advantage offered is that the system is easily extendible, so that further data agents and(More)
My research focuses on combining Distributed Data Mining (DDM) with MultiAgent Systems (MAS) benefiting from the possibilities offered by the MASs to improve overall DM performance. Data mining (DM) technology has emerged as a means for identifying patterns and trends from large quantities of data. The Data Mining technology normally adopts data integration(More)
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