Results 261 to 270 of about 2,962,161 (302)

Sequential Pattern Mining

2014
Sequential pattern mining, which discovers frequent subsequences as patterns in a sequence database, has been a focused theme in data mining research for over a decade. This problem has broad applications, such as mining customer purchase patterns and Web access patterns.
Wei Shen, Jianyong Wang, Jiawei Han
openaire   +1 more source

Mining and Ranking Generators of Sequential Patterns

Proceedings of the 2008 SIAM International Conference on Data Mining, 2008
Sequential pattern mining ¯rst proposed by Agrawal and Srikant has received intensive research due to its wide range applicability in many real-life domains. Various improvements have been proposed which include mining a closed set of sequential patterns.
LO, David, KHOO, Siau-Cheng, LI, Jinyan
openaire   +2 more sources

Mining Sequential Pattern Using DF2Ls

2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008
In this paper, based on SEP and IEP proposed in our previous work, we present two novel pruning strategies, DSEP (dynamic sequence extension pruning) and DIEP (dynamic item extension pruning), which can be used in all Apriori-like sequence mining algorithms or lattice-theoretic approaches. DSEP/DIEP uses DF2Ls (Dynamic Frequent 2-Sequence Lists), which
Xu Yusheng, Zhang Lanhui, Zhixin Ma, Li Lian, Xiaoyun Chen, Tharam S. Dillon   +5 more
openaire   +2 more sources

Multi-dimensional sequential pattern mining

Proceedings of the tenth international conference on Information and knowledge management, 2001
Sequential pattern mining, which finds the set of frequent subsequences in sequence databases, is an important data-mining task and has broad applications. Usually, sequence patterns are associated with different circumstances, and such circumstances form a multiple dimensional space. For example, customer purchase sequences are associated with region,
Helen Pinto, Jiawei Han 0001, Jian Pei 0001, Ke Wang 0001, Qiming Chen, Umeshwar Dayal   +5 more
openaire   +2 more sources

TaSPM: Targeted Sequential Pattern Mining

ACM Transactions on Knowledge Discovery From Data
Sequential pattern mining (SPM) is an important technique in the field of pattern mining, which has many applications in reality. Although many efficient SPM algorithms have been proposed, there are few studies that can focus on targeted tasks. Targeted querying of the concerned sequential patterns can not only reduce the number of patterns generated ...
Philip S. Yu, Wensheng Gan, Gengsen Huang   +2 more
exaly   +4 more sources

Mining sequential patterns in the B2B environment

Journal of Information Science, 2009
Sequential pattern mining is a powerful data mining technique for finding time-related behaviour in sequence databases. In this paper, we focus on mining sequential patterns in the business-to-business (B2B) environment. Because customers’ sequences in the B2B environment are very long, and almost all items are frequently purchased by all customers ...
Ya-Han Hu, Yen-Liang Chen, Kwei Tang
openaire   +2 more sources

MAIL: mining sequential patterns with wildcards

International Journal of Data Mining and Bioinformatics, 2013
Sequential pattern mining is an important research task in many domains, such as biological science. In this paper, we study the problem of mining frequent patterns from sequences with wildcards. The user can specify the gap constraints with flexibility. Given a subject sequence, a minimal support threshold and a gap constraint, we aim to find frequent
Fei Xie 0002, Xindong Wu 0001, Xuegang Hu, Jun Gao 0006, Dan Guo 0001, Yulian Fei, Ertian Hua   +6 more
openaire   +2 more sources

Sequential pattern mining algorithms review

Intelligent Data Analysis, 2012
From the beginning of sequential pattern mining to the present, this field has received important attention within the data mining area, because it has a wide application in several significant computational problems. Many algorithms have been created and several techniques have been used with the objective of improving the discovery of the frequent ...
José Kadir Febrer-Hernández, José Hernández-Palancar   +1 more
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Mining Sequential Patterns in Large Datasets

2006
A novel algorithm FFSPAN (Fast Frequent Sequential Pattern mining algorithm) is proposed in this paper. FFSPAN mines all the frequent sequential patterns in large datasets, and solves the problem of searching frequent sequences in a sequence database by searching frequent items or frequent itemsets.
Xiaoyu Chang, Chunguang Zhou, Zhe Wang 0007, Yanwen Li, Ping Hu   +4 more
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Sequential Pattern Mining in Multiple Streams

Fifth IEEE International Conference on Data Mining (ICDM'05), 2006
In this paper, we deal with mining sequential patterns in multiple data streams. Building on a state-of-the-art sequential pattern mining algorithm PrefixSpan for mining transaction databases, we propose MILE, an efficient algorithm to facilitate the mining process.
Gong Chen, Xindong Wu 0001, Xingquan Zhu 0001   +2 more
openaire   +2 more sources