Sukanya Yuenyong. The integration of fuzzy logic and graph search for sequential pattern mining. Master's Degree(Technology of Information System Management ). Mahidol University. : Mahidol University, 2008.
The integration of fuzzy logic and graph search for sequential pattern mining
Abstract:
Sequential pattern discovery is an important problem in data mining. In recent
years, there have been many researchers trying to find new techniques to extract the
sequential patterns from a large database. In this research, an effective way of the
integrating fuzzy logic and graph search methods to create the fuzzy logic and graph
search (FGS) algorithm for sequential pattern mining is proposed. The execution time
of the two graph search techniques was compared. It was found that the depth-first
search (DFS) takes less execution time than the breadth-first search (BFS). Also, the
FGS algorithm takes less execution time than the GST algorithm when the k-sequence
is greater than or equal to the 1-sequence (k≥2). The outcomes of the FGS algorithm
are more valuable than the GST algorithm because the quantitative values of each
transaction are considered. Finally, it was found that the FGS outcomes are
substantially lower than the GST outcomes. Sometimes, the reduction is an advantage
but it may not be so for all cases