Results 271 to 280 of about 14,528 (304)

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
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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
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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
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Mining Context Based Sequential Patterns

2005
Sequential pattern mining is an important task for Web usage mining. In this paper we generalize it to the problem of mining context based patterns, where context attributes may be introduced both for describing the complete sequence (e.g. characterizing user profiles) and for each element inside this sequence (describing circumstances for succeeding ...
Jerzy Stefanowski, Radoslaw Z. Ziembinski   +1 more
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Mining Sequential Patterns in Data Stream

2009
We present a new algorithm of mining sequential patterns in data stream. In recent years data stream emerges as a new data type in many applications. When processing data stream, the memory is fixed, new stream elements flow continuously. The stream data can not be paused or completely stored.
Qinhua Huang, Weimin Ouyang
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Mining Sequential Patterns with Item Constraints

2004
Mining sequential patterns is to discover sequential purchasing behaviors for most customers from a large amount of customer transactions. Past transaction data can be analyzed to discover customer purchasing behaviors. However, the size of the transaction database can be very large.
Show-Jane Yen, Yue-Shi Lee
openaire   +1 more source

Mining conditional discriminative sequential patterns

Information Sciences, 2019
Abstract Discriminative sequential pattern mining is one of the most important topics in pattern mining, which has a very wide range of applications. Discriminative sequential pattern mining is intended to extract sequential patterns with significant differences among different classes.
Zengyou He, Simeng Zhang, Feiyang Gu, Jun Wu   +3 more
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Efficient Weighted Sequential Pattern Mining

Expert Systems with Applications, 2023
Shaotao Chen, Jiahui Chen 0002, Shicheng Wan   +2 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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Mining Time-Gap Sequential Patterns

2012
Mining sequential patterns is to discover sequential purchasing behaviors for most of the customers from a large amount of customer transactions. An example of such a pattern is that most of the customers purchased item B after purchasing item A, and then they purchased item C after using item B.
Show-Jane Yen, Yue-Shi Lee
openaire   +1 more source