Efficient frequent sequence mining by a dynamic strategy switching algorithm

Authors:
Ding-Ying Chiu;Yi-Hung Wu;Arbee L. Chen
Affiliations:
Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan, ROC;Department of Information and Computer Engineering, Chung Yuan Christian University, Chung Li, Taiwan, ROC;Department of Computer Science, National Chengchi University, Taipei, Taiwan, ROC
Venue:
The VLDB Journal — The International Journal on Very Large Data Bases
Year:
2009

Citing 21
Cited 3

Data structures and algorithm analysis in C (2nd ed.)

Data structures and algorithm analysis in C (2nd ed.)
Efficient enumeration of frequent sequences

Proceedings of the seventh international conference on Information and knowledge management
Mining features for sequence classification

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
FreeSpan: frequent pattern-projected sequential pattern mining

Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences

Machine Learning
Multi-dimensional sequential pattern mining

Proceedings of the tenth international conference on Information and knowledge management
Mining long sequential patterns in a noisy environment

Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Mining sequential patterns with constraints in large databases

Proceedings of the eleventh international conference on Information and knowledge management
Prediction of Web Page Accesses by Proxy Server Log

World Wide Web
Mining Sequential Patterns with Regular Expression Constraints

IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns: Generalizations and Performance Improvements

EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Mining Sequential Patterns

ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth

Proceedings of the 17th International Conference on Data Engineering
FlExPat: Flexible Extraction of Sequential Patterns

ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Sequential PAttern mining using a bitmap representation

Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
An Efficient Algorithm for Mining Frequent Sequences by a New Strategy without Support Counting

ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach

IEEE Transactions on Knowledge and Data Engineering
Parallel mining of closed sequential patterns

Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Incremental Mining of Sequential Patterns over a Stream Sliding Window

ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Discovering nontrivial repeating patterns in music data

IEEE Transactions on Multimedia

The augmented itemset tree: a data structure for online maximum frequent pattern mining

DS'11 Proceedings of the 14th international conference on Discovery science
Extracting data records from web using suffix tree

Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics
Mining frequent itemsets from sparse data streams in limited memory environments

WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management

Quantified Score

Hi-index	0.00

Visualization

Abstract

Mining frequent sequences in large databases has been an important research topic. The main challenge of mining frequent sequences is the high processing cost due to the large amount of data. In this paper, we propose a novel strategy to find all the frequent sequences without having to compute the support counts of non-frequent sequences. The previous works prune candidate sequences based on the frequent sequences with shorter lengths, while our strategy prunes candidate sequences according to the non-frequent sequences with the same lengths. As a result, our strategy can cooperate with the previous works to achieve a better performance. We then identify three major strategies used in the previous works and combine them with our strategy into an efficient algorithm. The novelty of our algorithm lies in its ability to dynamically switch from a previous strategy to our new strategy in the mining process for a better performance. Experiment results show that our algorithm outperforms the previous ones under various parameter settings.