TY - GEN
T1 - A two-phase fuzzy mining approach
AU - Lin, Chun Wei
AU - Hong, Tzung Pei
AU - Lu, Wen Hsiang
PY - 2010/11/25
Y1 - 2010/11/25
N2 - In this paper, we propose a two-phase fuzzy mining approach based on a tree structure to discover fuzzy frequent itemsets from a quantitative database. A simple tree structure called the upper-bound fuzzy frequent-pattern tree (abbreviated as UBFFP tree) is designed to help achieve the purpose. The two-phase fuzzy mining approach can easily derive the upper-bound fuzzy supports of itemsets through the tree and prune unpromising itemsets in the first phase, and then finds the actual frequent fuzzy itemsets in the second phase. Experimental results also show the good performance of the proposed approach.
AB - In this paper, we propose a two-phase fuzzy mining approach based on a tree structure to discover fuzzy frequent itemsets from a quantitative database. A simple tree structure called the upper-bound fuzzy frequent-pattern tree (abbreviated as UBFFP tree) is designed to help achieve the purpose. The two-phase fuzzy mining approach can easily derive the upper-bound fuzzy supports of itemsets through the tree and prune unpromising itemsets in the first phase, and then finds the actual frequent fuzzy itemsets in the second phase. Experimental results also show the good performance of the proposed approach.
UR - http://www.scopus.com/inward/record.url?scp=78549255934&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=78549255934&partnerID=8YFLogxK
U2 - 10.1109/FUZZY.2010.5584373
DO - 10.1109/FUZZY.2010.5584373
M3 - Conference contribution
AN - SCOPUS:78549255934
SN - 9781424469208
T3 - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010
BT - 2010 IEEE World Congress on Computational Intelligence, WCCI 2010
T2 - 2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010
Y2 - 18 July 2010 through 23 July 2010
ER -