Paper Info
Title
Distributed fuzzy rule miner (DFRM)
Abstract
Nowadays scalability and capability of parallel execution are the most important characteristics for data mining algorithms due to the growing size of data sets. In this paper, a new distributed framework called DFRM is proposed to extract fuzzy rules from numerical data using a multi-agent approach. These extracted rules can be used for classification and decision making tasks. Scalability, self-organization and uncertainty handling are important characteristics of the proposed system. Scalability and self-organization are provided by autonomous agents in the learning process. Interaction among agents can lead to a more compact fuzzy rule base for decision making. Moreover the training samples are split equally between the agents randomly. Therefore each agent has a partial view of data set. Four UCI data sets are used to evaluate the proposed framework based on accuracy and rule base size. Experimental results show that the resulting distributed classification model maintains acceptable accuracy with fewer rules In addition, this model is robust against non-availability of training data.
Year
DOI
Venue
2013
10.1109/FUZZ-IEEE.2013.6622569
FUZZ-IEEE
Keywords
Field
DocType
uci data sets,parallel processing,fuzzy rules extraction,fuzzy set theory,parallel execution capability,classification tasks,rule base size,numerical data,autonomous agents,decision making,fuzzy rule extraction,learning (artificial intelligence),self-organization,data mining algorithms,pattern classification,distribute,multi-agent systems,dfrm,distributed fuzzy rule miner,distributed framework,uncertainty handling,agent,training data nonavailability,data mining,distributed classification model,decision making tasks,parallel execution scalability,learning process,multiagent approach,learning artificial intelligence,reliability,multi agent systems,fuzzy systems,accuracy,self organization,scalability
Data mining,Neuro-fuzzy,Fuzzy classification,Fuzzy set operations,Computer science,Fuzzy logic,Fuzzy set,Multi-agent system,Artificial intelligence,Machine learning,Fuzzy rule,Scalability
Conference
ISSN
ISBN
Citations 
1098-7584
978-1-4799-0020-6
0
PageRank 
References 
Authors
0.34
14
2
Name
Order
Citations
PageRank
Samane Sharif100.34
Mohammad R. Akbarzadeh-Totonchi212518.26