Relevant bibliographies by topics / Association Mining

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Association Mining'

Author: Grafiati
Published: 3 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "Association Mining"

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Ceglar, Aaron, and John F. Roddick. "Association mining." ACM Computing Surveys 38, no. 2 (2006): 5. http://dx.doi.org/10.1145/1132956.1132958.

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Pandey, Sachin. "Multilevel Association Rules in Data Mining." Journal of Advances and Scholarly Researches in Allied Education 15, no. 5 (2018): 74–78. http://dx.doi.org/10.29070/15/57517.

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Utthammajai, Krittithee, and Pakorn Leesutthipornchai. "Association Mining on Stock Index Indicators." International Journal of Computer and Communication Engineering 4, no. 1 (2015): 46–49. http://dx.doi.org/10.7763/ijcce.2015.v4.380.

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Kumar, Manoj, and Hemant Kumar Soni. "A Comparative Study of Tree-Based and Apriori-Based Approaches for Incremental Data Mining." International Journal of Engineering Research in Africa 23 (April 2016): 120–30. http://dx.doi.org/10.4028/www.scientific.net/jera.23.120.

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Association rule mining is an iterative and interactive process of discovering valid, novel, useful, understandable and hidden associations from the massive database. The Colossal databases require powerful and intelligent tools for analysis and discovery of frequent patterns and association rules. Several researchers have proposed the many algorithms for generating item sets and association rules for discovery of frequent patterns, and minning of the association rules. These proposals are validated on static data. A dynamic database may introduce some new association rules, which may be interesting and helpful in taking better business decisions. In association rule mining, the validation of performance and cost of the existing algorithms on incremental data are less explored. Hence, there is a strong need of comprehensive study and in-depth analysis of the existing proposals of association rule mining. In this paper, the existing tree-based algorithms for incremental data mining are presented and compared on the baisis of number of scans, structure, size and type of database. It is concluded that the Can-Tree approach dominates the other algorithms such as FP-Tree, FUFP-Tree, FELINE Alorithm with CATS-Tree etc.This study also highlights some hot issues and future research directions. This study also points out that there is a strong need for devising an efficient and new algorithm for incremental data mining.
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Thomas, Binu, and G. Raju. "A Novel Web Classification Algorithm Using Fuzzy Weighted Association Rules." ISRN Artificial Intelligence 2013 (December 19, 2013): 1–10. http://dx.doi.org/10.1155/2013/316913.

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In associative classification method, the rules generated from association rule mining are converted into classification rules. The concept of association rule mining can be extended in web mining environment to find associations between web pages visited together by the internet users in their browsing sessions. The weighted fuzzy association rule mining techniques are capable of finding natural associations between items by considering the significance of their presence in a transaction. The significance of an item in a transaction is usually referred as the weight of an item in the transaction and finding associations between such weighted items is called fuzzy weighted association rule mining. In this paper, we are presenting a novel web classification algorithm using the principles of fuzzy association rule mining to classify the web pages into different web categories, depending on the manner in which they appear in user sessions. The results are finally represented in the form of classification rules and these rules are compared with the result generated using famous Boolean Apriori association rule mining algorithm.
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Agrawal, Shivangee, and Nivedita Bairagi. "A Survey for Association Rule Mining in Data Mining." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (2017): 245. http://dx.doi.org/10.23956/ijarcsse.v7i8.58.

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Data mining, also identified as knowledge discovery in databases has well-known its place as an important and significant research area. The objective of data mining (DM) is to take out higher-level unknown detail from a great quantity of raw data. DM has been used in a variety of data domains. DM can be considered as an algorithmic method that takes data as input and yields patterns, such as classification rules, itemsets, association rules, or summaries, as output. The ’classical’ associations rule issue manages the age of association rules by support portraying a base level of confidence and support that the roduced rules should meet. The most standard and classical algorithm used for ARM is Apriori algorithm. It is used for delivering frequent itemsets for the database. The essential thought behind this algorithm is that numerous passes are made the database. The total usage of association rule strategies strengthens the knowledge management process and enables showcasing faculty to know their customers well to give better quality organizations. In this paper, the detailed description has been performed on the Genetic algorithm and FP-Growth with the applications of the Association Rule Mining.
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Han, Jianchao, and Mohsen Beheshti. "Discovering Both Positive and Negative Fuzzy Association Rules in Large Transaction Databases." Journal of Advanced Computational Intelligence and Intelligent Informatics 10, no. 3 (2006): 287–94. http://dx.doi.org/10.20965/jaciii.2006.p0287.

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Mining association rules is an important task of dara mining and knowledge discovery. Traditional association rules mining is built on transaction databases, which has some limitations. Two of these limitations are 1) each transaction merely contains binary items, meaning that an item either occurs in a transaction or not; 2) only positive association rules are discovered, while negative associations are ignored. Mining fuzzy association rules has been proposed to address the first limitation, while mining algorithms for negative association rules have been developed to resolve the second limitation. In this paper, we combine these two approaches to propose a novel approach for mining both positive and negative fuzzy association rules. The interestingness measure for both positive and negative fuzzy association rule is proposed, the algorithm for mining these rules is described, and an illustrative example is presented to demonstrate how the measure and the algorithm work.
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Giovanni, Daian Rottoli, and Merlino Hernan. "Spatial association discovery process using frequent subgraph mining." TELKOMNIKA Telecommunication, Computing, Electronics and Control 18, no. 4 (2020): 1884–91. https://doi.org/10.12928/TELKOMNIKA.v18i4.13858.

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Spatial associations are one of the most relevant kinds of patterns used by business intelligence regarding spatial data. Due to the characteristics of this particular type of information, different approaches have been proposed for spatial association mining. This wide variety of methods has entailed the need for a process to integrate the activities for association discovery, one that is easy to implement and flexible enough to be adapted to any particular situation, particularly for small and medium-size projects to guide the useful pattern discovery process. Thus, this work proposes an adaptable knowledge discovery process that uses graph theory to model different spatial relationships from multiple scenarios, and frequent subgraph mining to discover spatial associations. A proof of concept is presented using real data.
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Lu, Songfeng, Heping Hu, and Fan Li. "Mining weighted association rules." Intelligent Data Analysis 5, no. 3 (2001): 211–25. http://dx.doi.org/10.3233/ida-2001-5303.

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Defit, Sarjon. "Intelligent Mining Association Rules." International Journal of Computer Science and Information Technology 4, no. 4 (2012): 97–106. http://dx.doi.org/10.5121/ijcsit.2012.4409.

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Dissertations / Theses on the topic "Association Mining"

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Vithal, Kadam Omkar. "Novel applications of Association Rule Mining- Data Stream Mining." AUT University, 2009. http://hdl.handle.net/10292/826.

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From the advent of association rule mining, it has become one of the most researched areas of data exploration schemes. In recent years, implementing association rule mining methods in extracting rules from a continuous flow of voluminous data, known as Data Stream has generated immense interest due to its emerging applications such as network-traffic analysis, sensor-network data analysis. For such typical kinds of application domains, the facility to process such enormous amount of stream data in a single pass is critical.
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Wong, Wai-kit. "Security in association rule mining." Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/HKUTO/record/B39558903.

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Wong, Wai-kit, and 王偉傑. "Security in association rule mining." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39558903.

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Palanisamy, Senthil Kumar. "Association rule based classification." Link to electronic thesis, 2006. http://www.wpi.edu/Pubs/ETD/Available/etd-050306-131517/.

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Thesis (M.S.)--Worcester Polytechnic Institute.<br>Keywords: Itemset Pruning, Association Rules, Adaptive Minimal Support, Associative Classification, Classification. Includes bibliographical references (p.70-74).
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Cai, Chun Hing. "Mining association rules with weighted items." Hong Kong : Chinese University of Hong Kong, 1998. http://www.cse.cuhk.edu.hk/%7Ekdd/assoc%5Frule/thesis%5Fchcai.pdf.

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Thesis (M. Phil.)--Chinese University of Hong Kong, 1998.<br>Description based on contents viewed Mar. 13, 2007; title from title screen. Includes bibliographical references (p. 99-103). Also available in print.
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Zhou, Zequn. "Maintaining incremental data mining association rules." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ62311.pdf.

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Goulbourne, Graham. "Tree algorithms for mining association rules." Thesis, University of Liverpool, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.250218.

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With the increasing reliability of digital communication, the falling cost of hardware and increased computational power, the gathering and storage of data has become easier than at any other time in history. Commercial and public agencies are able to hold extensive records about all aspects of their operations. Witness the proliferation of point of sale (POS) transaction recording within retailing, digital storage of census data and computerized hospital records. Whilst the gathering of such data has uses in terms of answering specific queries and allowing visulisation of certain trends the volumes of data can hide significant patterns that would be impossible to locate manually. These patterns, once found, could provide an insight into customer behviour, demographic shifts and patient diagnosis hitherto unseen and unexpected. Remaining competitive in a modem business environment, or delivering services in a timely and cost effective manner for public services is a crucial part of modem economics. Analysis of the data held by an organisaton, by a system that "learns" can allow predictions to be made based on historical evidence. Users may guide the process but essentially the software is exploring the data unaided. The research described within this thesis develops current ideas regarding the exploration of large data volumes. Particular areas of research are the reduction of the search space within the dataset and the generation of rules which are deduced from the patterns within the data. These issues are discussed within an experimental framework which extracts information from binary data.
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Zhang, Ya Klein Cerry M. "Association rule mining in cooperative research." Diss., Columbia, Mo. : University of Missouri--Columbia, 2009. http://hdl.handle.net/10355/6540.

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The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file. Title from PDF of title page (University of Missouri--Columbia, viewed January 26, 2010). Thesis advisor: Dr. Cerry M. Klein. Includes bibliographical references.
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Icev, Aleksandar. "DARM distance-based association rule mining." Link to electronic thesis, 2003. http://www.wpi.edu/Pubs/ETD/Available/etd-0506103-132405.

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HajYasien, Ahmed. "Preserving Privacy in Association Rule Mining." Thesis, Griffith University, 2007. http://hdl.handle.net/10072/365286.

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With the development and penetration of data mining within different fields and disciplines, security and privacy concerns have emerged. Data mining technology which reveals patterns in large databases could compromise the information that an individual or an organization regards as private. The aim of privacy-preserving data mining is to find the right balance between maximizing analysis results (that are useful for the common good) and keeping the inferences that disclose private information about organizations or individuals at a minimum. In this thesis we present a new classification for privacy preserving data mining problems, we propose a new heuristic algorithm called the QIBC algorithm that improves the privacy of sensitive knowledge (as itemsets) by blocking more inference channels. We demonstrate the efficiency of the algorithm, we propose two techniques (item count and increasing cardinality) based on item-restriction that hide sensitive itemsets (and we perform experiments to compare the two techniques), we propose an efficient protocol that allows parties to share data in a private way with no restrictions and without loss of accuracy (and we demonstrate the efficiency of the protocol), and we review the literature of software engineering related to the associationrule mining domain and we suggest a list of considerations to achieve better privacy on software.<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>School of Information and Communication Technology<br>Faculty of Engineering and Information Technology<br>Full Text
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Books on the topic "Association Mining"

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Zhang, Chengqi, and Shichao Zhang, eds. Association Rule Mining. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-46027-6.

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Kaninis, A. Concurrent Mining of Association Rules. UMIST, 1997.

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Gkoulalas-Divanis, Aris, and Vassilios S. Verykios. Association Rule Hiding for Data Mining. Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6569-1.

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Gkoulalas-Divanis, Aris. Association rule hiding for data mining. Springer, 2010.

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Dass, Rajanish. Classification using association rules. Indian Institute of Management, 2008.

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Adamo, Jean-Marc. Data Mining for Association Rules and Sequential Patterns. Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4613-0085-4.

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1977-, Zhao Yanchang, Zhang Chengqi 1957-, and Cao Longbing 1969-, eds. Post-mining of association rules: Techniques for effective knowledge extraction. Information Science Reference, 2009.

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Foster, Verne. Verne Foster and the Nevada Mining Association: An oral history. Oral History Program, University of Nevada-Reno, 1988.

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Montana. Department of State Lands. Sapphire Village Permit Holders Association environmental assessment. Dept. of State Lands, 1993.

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Provincial Mining Association of British Columbia. Remedial legislation recommended by the Provincial Mining Association of British Columbia, 1903. The Association, 1997.

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Book chapters on the topic "Association Mining"

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Aggarwal, Charu C. "Association Pattern Mining." In Data Mining. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14142-8_4.

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Atkinson-Abutridy, John. "Association Rules Mining." In Text Analytics. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781003280996-5.

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Antonie, Luiza, Jundong Li, and Osmar Zaiane. "Negative Association Rules." In Frequent Pattern Mining. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07821-2_6.

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Zhou, Hong. "Association Analysis." In Learn Data Mining Through Excel. Apress, 2020. http://dx.doi.org/10.1007/978-1-4842-5982-5_10.

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Zhou, Hong. "Association Analysis." In Learn Data Mining Through Excel. Apress, 2023. http://dx.doi.org/10.1007/978-1-4842-9771-1_12.

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Aggarwal, Charu C. "Association Pattern Mining: Advanced Concepts." In Data Mining. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14142-8_5.

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Ventura, Sebastián, and José María Luna. "Class Association Rules." In Supervised Descriptive Pattern Mining. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98140-6_5.

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Shekhar, Shashi, and Hui Xiong. "Mining Spatial Association Patterns." In Encyclopedia of GIS. Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_788.

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Bramer, Max. "Association Rule Mining I." In Principles of Data Mining. Springer London, 2016. http://dx.doi.org/10.1007/978-1-4471-7307-6_16.

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Bramer, Max. "Association Rule Mining II." In Principles of Data Mining. Springer London, 2016. http://dx.doi.org/10.1007/978-1-4471-7307-6_17.

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Conference papers on the topic "Association Mining"

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Baez-Monroy, Vicente O., and Simon O'Keefe. "An Associative Memory for Association Rule Mining." In 2007 International Joint Conference on Neural Networks. IEEE, 2007. http://dx.doi.org/10.1109/ijcnn.2007.4371304.

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Jabas, Ahmad, Rama M. Garimella, and S. Ramachandram. "MANET mining: Mining step association rules." In 2008 5th IEEE International Conference on Mobile Ad Hoc and Sensor Systems (MASS). IEEE, 2008. http://dx.doi.org/10.1109/mahss.2008.4660089.

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Jabas, Ahmad, Rama Murtty Garimella, and Sirandas Ramachandram. "MANET Mining: Mining Temporal Association Rules." In 2008 IEEE International Symposium on Parallel and Distributed Processing with Applications. IEEE, 2008. http://dx.doi.org/10.1109/ispa.2008.66.

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Sermswatsri, P., and C. Srisa-an. "A neural-networks associative classification method for association rule mining." In DATA MINING AND MIS 2006. WIT Press, 2006. http://dx.doi.org/10.2495/data060101.

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Li, Jiuyong, Thuc Duy Le, Lin Liu, Jixue Liu, Zhou Jin, and Bingyu Sun. "Mining Causal Association Rules." In 2013 IEEE 13th International Conference on Data Mining Workshops (ICDMW). IEEE, 2013. http://dx.doi.org/10.1109/icdmw.2013.88.

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Chan, Keith C. C., and Wai-Ho Au. "Mining fuzzy association rules." In the sixth international conference. ACM Press, 1997. http://dx.doi.org/10.1145/266714.266898.

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Lee, Yue-Shi, and Show-Jane Yen. "Mining Utility Association Rules." In ICCAE 2018: 2018 10th International Conference on Computer and Automation Engineering. ACM, 2018. http://dx.doi.org/10.1145/3192975.3192987.

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Yang, Pu-Tai, Kai-Hao Yang, Ching-Chi Chen, and Shwu-Min Horng. "Subjective Association Rule Mining." In ICMLC 2018: 2018 10th International Conference on Machine Learning and Computing. ACM, 2018. http://dx.doi.org/10.1145/3195106.3195174.

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Selmane, Sid Ali, Rokia Missaoui, Omar Boussaid, and Fadila Bentayeb. "Mining Triadic Association Rules." In Second International Conference on Advanced Information Technologies and Applications. Academy & Industry Research Collaboration Center (AIRCC), 2013. http://dx.doi.org/10.5121/csit.2013.3825.

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Hidber, Christian. "Online association rule mining." In the 1999 ACM SIGMOD international conference. ACM Press, 1999. http://dx.doi.org/10.1145/304182.304195.

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Reports on the topic "Association Mining"

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Werdon, M. B. 2020 Alaska mining industry update (presentation): Association for Mineral Exploration Roundup, January 18, 2021. Alaska Division of Geological & Geophysical Surveys, 2021. http://dx.doi.org/10.14509/30592.

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Werdon, M. B., and Evan Twelker. 2021 Alaska mining industry update (presentation): Association for Mineral Exploration Roundup, January 31, 2022. Alaska Division of Geological & Geophysical Surveys, 2022. http://dx.doi.org/10.14509/30852.

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Werdon, M. B. 2022 Alaska mining industry update (presentation): Association for Mineral Exploration Roundup, Vancouver, BC, Canada, January 23-26, 2023. Alaska Division of Geological & Geophysical Surveys, 2023. http://dx.doi.org/10.14509/30952.

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Freeman, L. K., R. J. Newberry, J. E. Andrew, et al. Geologic setting of syngenetic and epigenetic deposits in the eastern Bonnifield mining district, Alaska (presentation): Alaska Miners Association, 22nd Annual Biennial Mining Conference, March 9-13, 2010. Alaska Division of Geological & Geophysical Surveys, 2013. http://dx.doi.org/10.14509/25279.

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Freeman, L. K. Alaska Mineral Industry 2013: A preliminary summary (presentation): Northwest Mining Association 119th Annual Meeting, Reno Nevada, December 2-6, 2013. Alaska Division of Geological & Geophysical Surveys, 2013. http://dx.doi.org/10.14509/26884.

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Freeman, L. K. Alaska's Mineral Resources 2015: A Preliminary summary (presentation): American Exploration and Mining Association 121st Annual Meeting, November 30 - December 4, 2015. Alaska Division of Geological & Geophysical Surveys, 2015. http://dx.doi.org/10.14509/29546.

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Szumigala, D. J. Alaska mining industry overview - Rocking the Arctic (presentation): Association for Mineral Exploration Roundup 2025, Vancouver, BC, Canada, January 20-23, 2025. Alaska Division of Geological & Geophysical Surveys, 2025. https://doi.org/10.14509/31473.

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Werdon, M. B. Alaska's mineral industry 2016: Mining, exploration and discoveries (presentation): Association for Mineral Exploration British Columbia Mineral Exploration Roundup, January 23-26, 2017. Alaska Division of Geological & Geophysical Surveys, 2017. http://dx.doi.org/10.14509/29711.

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Pinney, D. S., and D. M. Hopkins. Late Pleistocene paleoecology of Dalton Gulch, Tofty mining district, central Alaska (poster): Canadian Quaternary Association Meeting, Whitehorse, Yukon Territory, Canada, August 20-29, 2001. Alaska Division of Geological & Geophysical Surveys, 2001. http://dx.doi.org/10.14509/21841.

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Twelker, Evan, and L. E. Burns. New geochemical and geophysical data from the western Wrangellia minerals assessment area (presentation): Alaska Miners Association 24th Biennial Mining Conference, Fairbanks, Alaska April 7-13, 2014. Alaska Division of Geological & Geophysical Surveys, 2014. http://dx.doi.org/10.14509/27284.

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