Effective Database Transformation and Efficient Support Computation for Mining Sequential Patterns

• Published in: Database Systems for Advanced Applications pp. 163 - 174
• Main Authors: Cho, Chung-Wen, Wu, Yi-Hung, Chen, Arbee L. P.
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Computer science; control theory; systems; Data mining; Database transformation; Exact sciences and technology; Frequent k-sequences; Information systems. Data bases; Memory organisation. Data processing; Sequential patterns; Software; Tree structured method; Scalability; Sequential computation; Database; Transaction processing; Data mining; Execution time; Tree traversal
• ISBN: 3540253343; 9783540253341
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11408079_16

In this paper, we introduce a novel algorithm for mining sequential patterns from transaction databases. Since the FP-tree based approach is efficient in mining frequent itemsets, we adapt it to find frequent 1-sequences. For efficient frequent k-sequence mining, every frequent 1-sequence is encoded as a unique symbol and the database is transformed into one in the symbolic form. We observe that it is unnecessary to encode all the frequent 1-seqences, and make full use of the discovered frequent 1-sequences to transform the database into one with a smallest size. To discover the frequent k-sequences, we design a tree structure to store the candidates. Each customer sequence is then scanned to decide whether the candidates are frequent k-sequences. We propose a technique to avoid redundantly enumerating the identical k-subsequences from a customer sequence to speed up the process. Moreover, the tree structure is designed in a way such that the supports of the candidates can be incremented for a customer sequence by a single sequential traversal of the tree. The experiment results show that our approach outperforms the previous works in various aspects including the scalability and the execution time.