International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 3 | Mar -2017
p-ISSN: 2395-0072
www.irjet.net
Distributed Data mining using Multi Agent data Aliyi Huessein Nure Lecturer,Bule Hora University-Ethiopia Abstract: Multi-Agent Systems (MAS) offers architecture for distributed problem solving. Distributed Data Mining (DDM) algorithms focus on distributed problem solving tasks. In this paper we are discussing about how Multi-Agent Distributed Data Mining approach works on security issues and also discussed about the connection between distributed data mining (DDM) and multi agent system (MAS). Distributed data mining situate from the need of mining on decentralized data sources. Agent based computing aim is to deal with complex data systems has revealed opportunities to improve distributed data mining systems in a number of ways. In many applications the individual and collective behavior of the agents depends on the observed data from distributed sources. Keywords: Distributed data mining, Agent mining, KDD, Multi agent system.
I. Introduction: The Knowledge Discovery (KDD) process is a set of series focused on the discovery of knowledge within databases. The data mining is an application of a number of artificial intelligence, machine learning and statistics techniques to data. Data mining applied with huge amounts of data located at different sites, the amount of data can easily exceed the terabyte limit. Distributed Data Mining (DDM) is an emerging technology to speed performance and security issues because DDM avoids the transference across the network of very large volumes of data and the security issues occurs from network transferences. Distributed data mining (DDM) mines the data sources regardless of their physical locations. Distributed Data Mining (DDM) focus on recognizing patterns from distributed heterogeneous data bases in order to compose them within a distributed knowledge base and use for the purposes of decision making. A data mining agent is a pseudo-intelligent computer program designed to find out specific types of data, along with identifying patterns among those data types. These agents are typically used to detect trends in data, alerting organizations to paradigm shifts so effective strategies can be implemented to either take advantage of or minimize the damage from alterations in trends. In addition to reading patterns, data mining agents can also "pull" or "retrieve" relevant data from databases, alerting end-users to the presence of selected information. This paper deals the possible synergy between Multi Agent System (MAS) and Distributed Data Mining (DDM) technology. It particularly focuses on distributed agents, a problem finding increasing number of applications in networks, distributed information retrieval, and many other domains. Distributed data mining, in particular, for the purposes of distributed classification, has attended active research [1]. However, in this research the prime attention is to date paid to the algorithmic aspects of distributed data mining and combining decisions [2]. In this issue concerning cooperation protocols of distributed software components both in DDM and distributed classification as well as use of new technologies like multi-agent one is paid smaller attention.
Š 2017, IRJET
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