Sequence Pattern Mining in Data Streams

H. M. Hijawi; M. H. Saheb

doi:10.5539/cis.v8n3p64

Sequence Pattern Mining in Data Streams

Computer and Information Science ◽

10.5539/cis.v8n3p64 ◽

2015 ◽

Vol 8 (3) ◽

Cited By ~ 2

Author(s):

H. M. Hijawi ◽

M. H. Saheb

Keyword(s):

Data Streams ◽

Pattern Mining ◽

Sequence Pattern

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Sliding window based weighted maximal frequent pattern mining over data streams

Expert Systems with Applications ◽

10.1016/j.eswa.2013.07.094 ◽

2014 ◽

Vol 41 (2) ◽

pp. 694-708 ◽

Cited By ~ 64

Author(s):

Gangin Lee ◽

Unil Yun ◽

Keun Ho Ryu

Keyword(s):

Data Streams ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Sliding Window ◽

Frequent Pattern ◽

Maximal Frequent Pattern

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Deep learning frequent pattern mining on static semi structured data streams for improving fast speed and complex data streams

2021 7th International Conference on Optimization and Applications (ICOA) ◽

10.1109/icoa51614.2021.9442621 ◽

2021 ◽

Author(s):

G. Suseendran ◽

D. Balaganesh ◽

D. Akila ◽

Souvik Pal

Keyword(s):

Deep Learning ◽

Data Streams ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Structured Data ◽

Frequent Pattern ◽

Complex Data ◽

Fast Speed

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An Efficient Weighted Negative Sequence Pattern Mining Algorithm with Multiple Minimum Support

Advances in Intelligent Systems and Computing - International Conference on Applications and Techniques in Cyber Intelligence ATCI 2019 ◽

10.1007/978-3-030-25128-4_58 ◽

2019 ◽

pp. 460-469

Author(s):

Dongyuan Wang ◽

He Jiang ◽

Aixin Yang

Keyword(s):

Pattern Mining ◽

Sequence Pattern ◽

Negative Sequence ◽

Minimum Support ◽

Mining Algorithm

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

Wireless Personal Communications ◽

10.1007/s11277-018-5985-x ◽

2018 ◽

Vol 105 (2) ◽

pp. 673-689 ◽

Cited By ~ 1

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

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NOSEP: Nonoverlapping Sequence Pattern Mining With Gap Constraints

IEEE Transactions on Cybernetics ◽

10.1109/tcyb.2017.2750691 ◽

2018 ◽

Vol 48 (10) ◽

pp. 2809-2822 ◽

Cited By ~ 15

Author(s):

Youxi Wu ◽

Yao Tong ◽

Xingquan Zhu ◽

Xindong Wu

Keyword(s):

Pattern Mining ◽

Sequence Pattern

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Sequence Pattern Mining

Studies in Computational Intelligence - Foundations of Computational Intelligence Volume 4 ◽

10.1007/978-3-642-01088-0_2 ◽

2009 ◽

pp. 23-48

Author(s):

Huiyu Zhou ◽

Kaoru Shimada ◽

Shingo Mabu ◽

Kotaro Hirasawa

Keyword(s):

Pattern Mining ◽

Sequence Pattern

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EclatDS: An efficient sliding window based frequent pattern mining method for data streams

Intelligent Data Analysis ◽

10.3233/ida-2011-0483 ◽

2011 ◽

Vol 15 (4) ◽

pp. 571-587 ◽

Cited By ~ 11

Author(s):

Mahmood Deypir ◽

Mohammad Hadi Sadreddini

Keyword(s):

Data Streams ◽

Pattern Mining ◽

Frequent Pattern Mining ◽

Sliding Window ◽

Frequent Pattern ◽

Mining Method

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Protein Sequence Pattern Mining with Constraints

Knowledge Discovery in Databases: PKDD 2005 - Lecture Notes in Computer Science ◽

10.1007/11564126_14 ◽

2005 ◽

pp. 96-107 ◽

Cited By ~ 13

Author(s):

Pedro Gabriel Ferreira ◽

Paulo J. Azevedo

Keyword(s):

Protein Sequence ◽

Pattern Mining ◽

Sequence Pattern

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A Practice Probability Frequent Pattern Mining Method over Transactional Uncertain Data Streams

Ubiquitous Intelligence and Computing - Lecture Notes in Computer Science ◽

10.1007/978-3-642-23641-9_45 ◽

2011 ◽

pp. 563-575 ◽

Cited By ~ 1

Author(s):

Guoqiong Liao ◽

Linqing Wu ◽

Changxuan Wan ◽

Naixue Xiong

Keyword(s):

Data Streams ◽

Pattern Mining ◽

Uncertain Data ◽

Frequent Pattern Mining ◽

Frequent Pattern ◽

Mining Method ◽

Uncertain Data Streams

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Sequence Pattern Mining for Web Logs

Pattern Discovery Using Sequence Data Mining ◽

10.4018/978-1-61350-056-9.ch014 ◽

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.

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