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Books on the topic 'Mining Techniques and Pattern Discovery'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Yang, Jian. Intelligent Science and Intelligent Data Engineering: Third Sino-foreign-interchange Workshop, IScIDE 2012, Nanjing, China, October 15-17, 2012. Revised Selected Papers. Springer Berlin Heidelberg, 2013.

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2

Yadav, Vikash, Anil Kumar Dubey, Harivans Pratap Singh, Gaurav Dubey, and Erma Suryani. Process Mining Techniques for Pattern Recognition. CRC Press, 2022. http://dx.doi.org/10.1201/9781003169550.

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3

Pal, Nikhil R., and Lakhmi Jain, eds. Advanced Techniques in Knowledge Discovery and Data Mining. Springer London, 2005. http://dx.doi.org/10.1007/1-84628-183-0.

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4

R, Pal Nikhil, and Jain L. C, eds. Advanced techniques in knowledge discovery and data mining. Springer-Verlag, 2004.

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5

R, Pal Nikhil, and Jain L. C, eds. Advanced techniques in knowledge discovery and data mining. Springer-Verlag, 2005.

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6

L, Wang Jason T., Shapiro Bruce A, and Shasha Dennis Elliott, eds. Pattern discovery in biomolecular data: Tools, techniques, and applications. Oxford University, 1999.

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7

Kumar, Pradeep. Pattern discovery using sequence data mining: Applications and studies. Information Science Reference, 2012.

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8

Alam, Shafiq. Biologically-inspired techniques for knowledge discovery and data mining. Information Science Reference, an imprint of IGI Global, 2014.

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9

Atzmüller, Martin. Knowledge-intensive subgroup mining: Techniques for automatic and interactive discovery. Aka, Akademische Verlagsgsellschaft, 2007.

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10

May, Michael. Ubiquitous Knowledge Discovery: Challenges, Techniques, Applications. Springer Berlin Heidelberg, 2010.

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11

PhD, Mitra Pabitra, ed. Pattern recognition algorithms for data mining: Scalability, knowledge discovery and soft granular computing. Chapman & Hall/CRC, 2004.

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12

Cambria, Erik. Sentic Computing: Techniques, Tools, and Applications. Springer Netherlands, 2012.

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13

service), SpringerLink (Online, ed. Bisociative Knowledge Discovery: An Introduction to Concept, Algorithms, Tools, and Applications. Springer Berlin Heidelberg, 2012.

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14

Hongjun, Lu, Motoda Hiroshi, and Liu Huan 1958-, eds. KDD, techniques and applications: Proceedings of the First Pacific-Asia Conference on Knowledge Discovery and Data Mining, 23-24 Feb. 97. World Scientific, 1997.

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15

Gagalowicz, André. Computer Vision/Computer Graphics Collaboration Techniques: 5th International Conference, MIRAGE 2011, Rocquencourt, France, October 10-11, 2011. Proceedings. Springer-Verlag GmbH Berlin Heidelberg, 2011.

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16

DaWaK 2010 (2010 Bilbao, Spain). Data warehousing and knowledge discovery: 12th international conference, DaWaK 2010, Bilbao, Spain, August/September 2010 : proceedings. Springer, 2010.

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17

Ernest, Fokoué, Zhang Hao Helen, and SpringerLink (Online service), eds. Principles and Theory for Data Mining and Machine Learning. Springer-Verlag New York, 2009.

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18

Bramer, M. A. (Max A.), 1948-, Petridis Miltos, Hopgood Adrian A, and British Computer Society. Specialist Group on Artificial Intelligence, eds. Proceedings of AI-2010, the Thirtieth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence: Incorporating applications and innovations in intelligent systems XVIII. Springer, 2011.

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19

Peek, Niels. Artificial Intelligence in Medicine: 14th Conference on Artificial Intelligence in Medicine, AIME 2013, Murcia, Spain, May 29 – June 1, 2013. Proceedings. Springer Berlin Heidelberg, 2013.

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20

Ochoa, Jesús Ariel Carrasco. Pattern Recognition: 5th Mexican Conference, MCPR 2013, Querétaro, Mexico, June 26-29, 2013. Proceedings. Springer Berlin Heidelberg, 2013.

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21

Christen, Peter. Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer, 2014.

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22

Christen, Peter. Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer, 2012.

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23

Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning (Springer Texts in Statistics). Springer, 2009.

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24

Li, Ying, Zhi-Hua Zhou, Yanning Zhang, and Changshui Zhang. Intelligent Science and Intelligent Data Engineering: Second Sino-foreign-interchange Workshop, IScIDE 2011, Xi'an, China, October 23-25, 2011, ... Springer, 2012.

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25

Fang, Fang, Jian Yang, and Changyin Sun. Intelligent Science and Intelligent Data Engineering: Third Sino-foreign-interchange Workshop, IScIDE 2012, Nanjing, China, October 15-17, 2012, ... Springer, 2013.

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26

Process Mining Techniques for Pattern Recognition. Taylor & Francis Group, 2022.

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27

Frequent Pattern Mining. Springer, 2014.

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28

Pal, Nikhil. Advanced Techniques in Knowledge Discovery and Data Mining. Springer London, Limited, 2007.

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29

Pal, Nikhil. Advanced Techniques in Knowledge Discovery and Data Mining. Springer, 2014.

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30

(Editor), H. J. Lu, H. Liu (Editor), and H. Motoda (Editor), eds. Knowledge Discovery and Data Mining: Techniques and Applications. World Scientific Publishing Co Pte Ltd, 1997.

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31

Pattern Discovery in Biomolecular Data: Tools, Techniques, and Applications. Oxford University Press, USA, 1999.

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32

Shapiro, Bruce A., Dennis Shasha, and Jason T. L. Wang. Pattern Discovery in Biomolecular Data: Tools, Techniques, and Applications. Oxford University Press, 1999.

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33

Pattern Mining and Concept Discovery for Multimodal Content Analysis. [publisher not identified], 2016.

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34

Swiniarski, Roman W., Witold Pedrycz, Krzysztof J. J. Cios, and Lukasz Andrzej Kurgan. Data Mining: A Knowledge Discovery Approach. Springer, 2010.

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35

Yadav, Vikash, Erma Suryani, Anil Kumar Dubey, Harivans Pratap Singh, and Gaurav Dubey. Process Mining Techniques for Pattern Recognition: Concepts, Theory, and Practice. Taylor & Francis Group, 2022.

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36

Yadav, Vikash, Erma Suryani, Anil Kumar Dubey, Harivans Pratap Singh, and Gaurav Dubey. Process Mining Techniques for Pattern Recognition: Concepts, Theory, and Practice. Taylor & Francis Group, 2022.

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37

Yadav, Vikash. Process Mining Techniques for Pattern Recognition: Concepts, Theory, and Practice. CRC Press LLC, 2022.

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38

Yadav, Vikash, Erma Suryani, Anil Kumar Dubey, Harivans Pratap Singh, and Gaurav Dubey. Process Mining Techniques for Pattern Recognition: Concepts, Theory, and Practice. Taylor & Francis Group, 2022.

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39

Usman, Muhammad. Improving Knowledge Discovery Through the Integration of Data Mining Techniques. IGI Global, 2015.

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40

“Data Mining Concepts & Techniques”. 3rd ed. Morgan Kaufmann Publishers, 2011.

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41

Scientific Data Mining and Knowledge Discovery. Springer, 2009.

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42

Liao, Felix. Smart Data Discovery Using SAS Viya: Powerful Techniques for Deeper Insights. SAS Institute, 2020.

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43

Triantaphyllou, Evangelos, and Giovanni Felici, eds. Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques. Springer US, 2006. http://dx.doi.org/10.1007/0-387-34296-6.

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44

Triantaphyllou, Evangelos, and Giovanni Felici. Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques. Springer, 2011.

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45

Triantaphyllou, Evangelos, and Giovanni Felici. Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques. Springer, 2006.

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46

Ferrari, Elena, and Francesco Bonchi. Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques. Taylor & Francis Group, 2010.

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47

Ferrari, Elena, and Francesco Bonchi. Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques. Taylor & Francis Group, 2010.

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48

Privacy-aware knowledge discovery: Novel applications and new techniques. CRC Press, 2011.

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49

Wang, Jason T. L., Bruce A. Shapiro, and Dennis Shasha, eds. Pattern Discovery in Biomolecular Data. Oxford University Press, 1999. http://dx.doi.org/10.1093/oso/9780195119404.001.0001.

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Abstract:

Finding patterns in biomolecular data, particularly in DNA and RNA, is at the center of modern biological research. These data are complex and growing rapidly, so the search for patterns requires increasingly sophisticated computer methods. Pattern Discovery in Biomolecular Data provides a clear, up-to-date summary of the principal techniques. Each chapter is self-contained, and the techniques are drawn from many fields, including graph theory, information theory, statistics, genetic algorithms, computer visualization, and vision. Since pattern searches often benefit from multiple approaches,

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50

Proceedings of the First International Workshop on Novel Data Stream Pattern Mining Techniques. Association for Computing Machinery, 2010.

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