Mining Multiple Fuzzy Frequent Patterns with Compressed List Structures
Jerry Chun‐Wei Lin, Jimmy Ming‐Tai Wu, Youcef Djenouri, Gautam Srivastava, Tzung‐Pei Hong
Abstract
Fuzzy-set theory was invented to represent more meaningful representations of knowledge for human reasoning, which can also be applied and utilized for handling the quantitative database. In this paper, an efficient fuzzy mining (EFM) algorithm is presented to fast discover the multiple fuzzy frequent patterns from quantitative databases under type-2 fuzzy-set theory. A compressed fuzzy-list (CFL)-structure is developed to maintain complete information for rule generation. Two pruning techniques are developed to reduce the search space and speed up mining progress. Several experiments are carried out for the purpose of verifying the efficiency and effectiveness of the designed approach in terms of runtime and the number of examined nodes under different minimum support thresholds and the results indicated the designed EFM achieves the best performance compared to the existing models.