NDLI: An efficient agent based framework for distributed medical databases (Revised August 2011)

Content Provider	IEEE Xplore Digital Library
Author	Atteya, W.A. Dahal, K. Hossain, M.A.
Copyright Year	2011
Description	Author affiliation: Sch. of Comput., Inf. & Media, Bradford Univ., Bradford, UK (Atteya, W.A.; Dahal, K.) \|\| Sch. of Comput., Eng. & Inf. Sci., Northumbria Univ., Newcastle uopn Tyne, UK (Hossain, M.A.) Many algorithms have been proposed for the discovery of association rules. The efficiency of these algorithms needs to be improved to handle real-world large datasets. Specifically, for data stored in heterogeneous and geographically distributed healthcare centers. This efficiency can be determined mainly by three factors. The way candidates are generated, the way their supports are counted and the data structure used. Most papers focus on the first and the second factors while few focus on the underlying data structures. In this paper, we present a distributed Multi-Agent based algorithm for mining association rules in distributed environments. The distributed MAS algorithm uses Bit vector data structure that was proved to have better performance in centralized environments. The algorithm is implemented in the context of Multi-Agent systems and complies with global communication standard Foundation for Intelligent Physical Agents (FIPA). The distributed Multi-Agent based algorithm with its new data structure improves implementations reported in the literature that were based on Apriori. The algorithm has better performance over Apriori-like algorithms.
File Size	415420
File Format	PDF
ISBN	9781467302470
e-ISBN	9781467302487
DOI	10.1109/SKIMA.2011.6163198
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-09-08
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Itemsets Multi-Agent System Distributed databases Data structures Association Rules Association rules Distributed Data Mining
Content Type	Text
Resource Type	Article

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