1. Главная
  2. Ресурсы
  3. Библиотечный поиск
Mining sequentialpatterns bypattern-growth: thePrefixSpan approach Jian PeiJiawei HanBehzad Mortazavi-Asl2004 год

Mining sequential patterns by pattern-growth: the PrefixSpan approach
статья из журнала

Полный текстСтраница публикацииПубликация в OpenAlex
Аннотация: Sequential pattern mining is an important data mining problem with broad applications. However, it is also a difficult problem since the mining may have to generate or examine a combinatorially explosive number of intermediate subsequences. Most of the previously developed sequential pattern mining methods, such as GSP, explore a candidate generation-and-test approach [R. Agrawal et al. (1994)] to reduce the number of candidates to be examined. However, this approach may not be efficient in mining large sequence databases having numerous patterns and/or long patterns. In this paper, we propose a projection-based, sequential pattern-growth approach for efficient mining of sequential patterns. In this approach, a sequence database is recursively projected into a set of smaller projected databases, and sequential patterns are grown in each projected database by exploring only locally frequent fragments. Based on an initial study of the pattern growth-based sequential pattern mining, FreeSpan [J. Han et al. (2000)], we propose a more efficient method, called PSP, which offers ordered growth and reduced projected databases. To further improve the performance, a pseudoprojection technique is developed in PrefixSpan. A comprehensive performance study shows that PrefixSpan, in most cases, outperforms the a priori-based algorithm GSP, FreeSpan, and SPADE [M. Zaki, (2001)] (a sequential pattern mining algorithm that adopts vertical data format), and PrefixSpan integrated with pseudoprojection is the fastest among all the tested algorithms. Furthermore, this mining methodology can be extended to mining sequential patterns with user-specified constraints. The high promise of the pattern-growth approach may lead to its further extension toward efficient mining of other kinds of frequent patterns, such as frequent substructures.
Год издания: 2004
Авторы: Jian Pei, Jiawei Han, Behzad Mortazavi-Asl, Jianyong Wang, H. Pinto, Qiming Chen, Umeshwar Dayal, Meichun Hsu
Издательство: IEEE Computer Society
Источник: IEEE Transactions on Knowledge and Data Engineering
Ключевые слова: Data Mining Algorithms and Applications, Rough Sets and Fuzzy Logic, Algorithms and Data Compression
Другие ссылки: IEEE Transactions on Knowledge and Data Engineering (HTML)
CiteSeer X (The Pennsylvania State University) (PDF)
CiteSeer X (The Pennsylvania State University) (HTML)