Paper Info
Title
Land evaluation algorithms based on simplified fuzzy classification association rules and grouping fuzzy decision
Abstract
To improve the intelligibility and efficiency of knowledge expression for the land evaluation, a land evaluation method combining simplified fuzzy classification association rules with fuzzy decision is proposed in this paper. To reduce the complexity of the land evaluation models and improve the efficiency and intelligibility of fuzzy classification association rules further, an algorithm to eliminate redundant rules for obtaining the simplified fuzzy classification association rules is presented. In addition, considering the challenge of a few samples that are difficult to classify the process of fuzzy decision, an iterative algorithm for grouping fuzzy decision for datasets is discussed. The results of experiments demonstrate that by using only 32 simplified fuzzy classification association rules, accuracy of area of land evaluation can reach 92.2835 percent. It provides a higher precision with the accuracy improved by 5.0039%, comparing with the results of the method combining 32 original fuzzy classification association rules with fuzzy decision when minimum support is 0.005.
Year
DOI
Venue
2010
10.1109/FSKD.2010.5569704
FSKD
Keywords
DocType
Volume
fuzzy classification association rules,fuzzy set theory,simplified rules,grouping fuzzy decision,knowledge expression,land evaluation,decision making,pattern classification,fuzzy decision,datasets,land evaluation method,iterative algorithm,data mining,iterative methods,association rules,classification algorithms,fuzzy classification,association rule,databases,algorithm design and analysis,accuracy
Conference
1
ISBN
Citations 
PageRank 
978-1-4244-5931-5
0
0.34
References 
Authors
5
3
Name
Order
Citations
PageRank
Ting Li100.34
Jingfeng Yang2618.34
Zhimin Chen3397.93