Sequence mining

Results: 85



#Item
71Computational Biology and Chemistry[removed]–416  Database Note iProLINK: an integrated protein resource for literature mining Zhang-Zhi Hua , Inderjeet Manib , Vincent Hermosoa , Hongfang Liuc , Cathy H. Wua,∗

Computational Biology and Chemistry[removed]–416 Database Note iProLINK: an integrated protein resource for literature mining Zhang-Zhi Hua , Inderjeet Manib , Vincent Hermosoa , Hongfang Liuc , Cathy H. Wua,∗

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Source URL: pir.georgetown.edu

Language: English - Date: 2005-11-21 12:39:29
72Reference Series  Four Stages in Idaho’s Mining Development Number 4 These stages usually form a temporal sequence (although two or three of them often overlap greatly) in the sense that one often led to the next. The

Reference Series Four Stages in Idaho’s Mining Development Number 4 These stages usually form a temporal sequence (although two or three of them often overlap greatly) in the sense that one often led to the next. The

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Source URL: www.history.idaho.gov

Language: English - Date: 2014-03-04 10:02:59
73Introduction to arules – A computational environment for mining association rules and frequent item sets Michael Hahsler  Bettina Gru

Introduction to arules – A computational environment for mining association rules and frequent item sets Michael Hahsler Bettina Gru

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Source URL: cran.r-project.org

Language: English - Date: 2014-06-17 04:54:36
74Abstraction-based probabilistic models for sequence classification by Cornelia Caragea

Abstraction-based probabilistic models for sequence classification by Cornelia Caragea

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Source URL: www.cs.iastate.edu

Language: English - Date: 2011-08-31 00:19:53
751  ANGELI, DAVISON: LIVE FEATURE CLUSTERING IN VIDEO

1 ANGELI, DAVISON: LIVE FEATURE CLUSTERING IN VIDEO

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Source URL: www.doc.ic.ac.uk

Language: English - Date: 2010-09-13 10:26:24
76An Early Illness Recognition Framework Using a Temporal Smith Waterman Algorithm and NLP Zahra Hajihashemi, MS, Mihail Popescu, PhD University of Missouri, Columbia, MO Abstract In this paper we propose a framework for d

An Early Illness Recognition Framework Using a Temporal Smith Waterman Algorithm and NLP Zahra Hajihashemi, MS, Mihail Popescu, PhD University of Missouri, Columbia, MO Abstract In this paper we propose a framework for d

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Source URL: www.ncbi.nlm.nih.gov

Language: English
77Weitschek et al. BioData Mining 2014, 7:4 http://www.biodatamining.org/content/7/1/4

Weitschek et al. BioData Mining 2014, 7:4 http://www.biodatamining.org/content/7/1/4

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Source URL: www.ncbi.nlm.nih.gov

Language: English
78Multidimensional Sequence Alignment Methods for Activity Pattern Analysis: A comparison of dynamic programming and genetic algorithms Chang-Hyeon Joh, Theo A. Arentze and Harry J.P. Timmermans1

Multidimensional Sequence Alignment Methods for Activity Pattern Analysis: A comparison of dynamic programming and genetic algorithms Chang-Hyeon Joh, Theo A. Arentze and Harry J.P. Timmermans1

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Source URL: www-sre.wu-wien.ac.at

Language: English - Date: 2012-12-28 04:53:30
79Microsoft Word - PM_32_Blanchard FN

Microsoft Word - PM_32_Blanchard FN

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Source URL: www.concepts-methods.org

Language: English - Date: 2011-11-14 12:15:36
80Mining event histories: A social scientist view Gilbert Ritschard1 1

Mining event histories: A social scientist view Gilbert Ritschard1 1

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Source URL: mephisto.unige.ch

Language: English - Date: 2011-10-06 09:30:47