Improving DDM Performance By Combining Distributed Data Mining and Multi-Agent System
Sitanath Biswas1, Subrat S. Pattnaik2, Sweta Acharya3
1Sitanath Biswas, Department Of Comp.Sc.&Engg, Gandhi Institute For Technology, Bhubaneswar,Orissa, India.
2Subrat S. Pattnaik, Department Of Comp.Sc.&Engg, Gandhi Institute For Technology, Bhubaneswar,Orissa, India.
3Sweta Acharya, College of Engineering and Technology, Bhubaneswar,Orissa,India
Manuscript received on September 01, 2012. | Revised Manuscript received on September 02, 2012. | Manuscript published on September 05, 2012. | PP: 215-221 | Volume-2 Issue-4, September 2012. | Retrieval Number: D0935082412/2012©BEIESP
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Autonomous agents and multi agent systems (or agents) and data mining and knowledge discovery (or data mining) are two of the most active areas in information technology. Ongoing research has revealed a number of intrinsic challenges and problems facing each area, which can’t be addressed solely within the confines of the respective discipline. A profound insight of bringing these two communities together has unveiled a tremendous potential for new opportunities and wider applications through the synergy of agents and data mining. With increasing interest in this synergy, agent mining is emerging as a new research field studying the interaction and integration of agents and data mining. In this paper, we give an overall perspective of the driving forces, theoretical underpinnings, main research issues, and application domains of this field, while addressing the state-of-the-art of agent mining research and development. Our review is divided into three key research topics: agent-driven data mining, data mining-driven agents, and joint issues in the synergy of agents and data mining. This new and promising field exhibits a great potential for groundbreaking work from foundational, technological and practical perspectives.
Keywords: Multi-agent systems, distributed data mining, clustering, privacy, agent- based ddm.