Sequence Pattern Mining

2009 ◽  
pp. 23-48
Author(s):  
Huiyu Zhou ◽  
Kaoru Shimada ◽  
Shingo Mabu ◽  
Kotaro Hirasawa
Keyword(s):  
Pattern Mining ◽  
Sequence Pattern

Batch-Free Event Sequence Pattern Mining for Communication Stream Data with Instant and Persistent Events

2018 ◽  
Vol 105 (2) ◽  
pp. 673-689 ◽  
Author(s):  
Keon Myung Lee ◽  
Chan Sik Han ◽  
Joong Nam Jun ◽  
Jee Hyong Lee ◽  
Sang Ho Lee
Keyword(s):  
Pattern Mining ◽  
Event Sequence ◽  
Stream Data ◽  
Sequence Pattern

NOSEP: Nonoverlapping Sequence Pattern Mining With Gap Constraints

2018 ◽  
Vol 48 (10) ◽  
pp. 2809-2822 ◽  
Author(s):  
Youxi Wu ◽  
Yao Tong ◽  
Xingquan Zhu ◽  
Xindong Wu
Keyword(s):  
Pattern Mining ◽  
Sequence Pattern

Sequence Pattern Mining for Web Logs

2012 ◽  
pp. 237-243
Author(s):  
Pradeep Kumar ◽  
Raju S. Bapi ◽  
P. Radha Krishna
Keyword(s):  
Pattern Mining ◽  
Time And Space ◽  
Sequence Pattern ◽  
Interestingness Measures ◽  
Web Logs ◽  
Space Cost ◽  
Reduced Time

Interestingness measures play an important role in finding frequently occurring patterns, regardless of the kind of patterns being mined. In this work, we propose variation to the AprioriALL Algorithm, which is commonly used for the sequence pattern mining. The proposed variation adds up the measure interest during every step of candidate generation to reduce the number of candidates thus resulting in reduced time and space cost. The proposed algorithm derives the patterns which are qualified and more of interest to the user. The algorithm, by using the interest, measure limits the size the candidates set whenever it is produced by giving the user more importance to get the desired patterns.

scholarly journals Sequence Pattern Mining in Data Streams

2015 ◽  
Vol 8 (3) ◽  
Author(s):  
H. M. Hijawi ◽  
M. H. Saheb
Keyword(s):  
Data Streams ◽  
Pattern Mining ◽  
Sequence Pattern
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