Title :
Mining complete fuzzy frequent itemsets by tree structures
Author :
Hong, Tzung-Pei ; Lin, Tsung-Ching ; Tsung-Ching Lin
Author_Institution :
Dept. of Comput. Sci. & Inf. Eng., Nat. Univ. of Kaohsiung, Kaohsiung, Taiwan
Abstract :
In this paper, we attempt to extend the fuzzy FP-tree mining process to mine all fuzzy frequent itemsets, instead of only the representative linguistic terms, from a set of quantitative transactions. A multiple fuzzy-term FP (MFFP) tree with the consideration of fuzzy operations is proposed to help the execution of the fuzzy mining process. The corresponding MFFP-growth mining algorithm is also designed to derive all fuzzy frequent itemsets from the tree structure by fuzzy operations. Experimental results also show the performance of the proposed approach.
Keywords :
data mining; fuzzy set theory; tree data structures; fuzzy frequent itemsets mining; multiple fuzzy term FP tree mining process; tree structure; Association rules; Itemsets; fuzzy data mining; fuzzy frequent itemset; fuzzy region; fuzzy set; quantitative value;
Conference_Titel :
Systems Man and Cybernetics (SMC), 2010 IEEE International Conference on
Conference_Location :
Istanbul
Print_ISBN :
978-1-4244-6586-6
DOI :
10.1109/ICSMC.2010.5642016