Mining Sequential Patterns Using the Integration of Fuzzy Logic and Graph Search Techniques Pisit Phokharatkul Department of Computer Engineering, Faculty of Engineering, Mahidol University, Nakhon Pathom, 73170, Thailand Email: egpph@mahidol.ac.th Sukanya Yuenyong Information Management Department, KASIKORNBANK PCL, RatBurana, Bangkok 10140, Thailand Email: sukanya.yu@kasikornbank.com, lukkaew@live.com Abstract Sequential pattern discovery is an important problem in data mining. In recent years, the researchers have been to find the 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. Key Words: data mining, sequential pattern, fuzzy logic, graph search. 1. Introduction Nowadays, we use computers to collect data in various formats such as text file, database and XML formats. The advantages of data collection are more than search and review. The knowledge can be extracted from the existing data; call data mining. Data mining is the process of extracting interesting information or patterns from large information repositories. There are many types of data mining. Sequential pattern discovery is an important problem in data mining. In recent years there have been and continue to be many researchers trying to find new techniques to extract the sequential patterns from large database. They used difference algorithms such as: DSG algorithm [1], fuzzy algorithm [2], the algorithm mining path traversal pattern [3], and Generalized Sequential Pattern (GSP) [4]. This paper studies the integrating fuzzy logic and graph search algorithm for sequential pattern mining. There are two important things in the sequential pattern mining. First is the performance of execution time. Secondary is the result, how can extract the most useful sequential patterns. This problem can construe into many issues depending on the interest of an individual such as: case of inventory, mining sequential pattern use for predicting the consumer purchasing behavior. The outcome of sequential pattern mining can predict what the next product or group of products will be purchased when the product or group of products already purchased is known. Although the algorithms always extract the sequential pattern, the user will always desire a better pattern. However, new algorithms must continue to solve the two important constraints of execution time and give a better result. 2. Data mining Data mining [1] is the process of extracting interesting (non-trivial, implicit, previously unknown and potentially useful) information or pattern from large information repositories such as: relational database, data warehouse, XML repository, etc. Thus, data mining is known as one of the core processes of knowledge discovery in database. The processes of the knowledge discovery method consists the steps is shown in Figure 1. First, the data source comes from different databases, which may have some inconsistence and duplications. The system cleans the data source by removing some noises or makes some compromises. And the integrated data sources can be stored in the Proceedings of the Second International Conference on Knowledge and Smart Technologies 2010 (July, 24-25, 2010) 12