An UBMFFP Tree for Mining Multiple Fuzzy Frequent Itemsets

作者：Lin, Jerry Chun Wei^*; Hong, Tzung Pei; Lin, Tsung Ching; Pan, Shing Tai

来源：International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 2015, 23(6): 861-879.

DOI：10.1142/S0218488515500385

• Fuzzy data mining
• fuzzy set
• two-phase mining
• frequent-pattern (FP)-tree
• multiple fuzzy frequent itemset
• UBMFFP-tree

Frequent itemsets are useful for discovering interesting associations hidden in large databases. Many mining algorithms use data with binary attributes to represent the occurrence of items and find frequent itemsets. However, many real-world applications provide a richer source of transactions with quantitative values. The fuzzy frequent-pattern tree algorithm was thus proposed for extracting fuzzy frequent itemsets from the quantitative transactions. In this paper, a tree structure called the upper-bound multiple fuzzy frequent-pattern (UBMFFP)-tree is designed for improving the pruning effect in the mining process. A two-phase fuzzy mining approach based on the tree structure is also proposed to obtain the complete fuzzy frequent itemsets from a quantitative database. The proposed fuzzy mining approach recursively and efficiently finds the upper-bound fuzzy counts of itemsets with the aid of the tree structure. It prunes unpromising itemsets in the first phase, and then finds the actual fuzzy frequent itemsets in the second phase. Experimental results indicate that the proposed UBMFFP-tree algorithm has good performance in terms of execution time and number of tree nodes.

• 出版日期2015-12
• 单位深圳大学; 中山大学