Relevant bibliographies by topics / Association rule data mining

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

Academic literature on the topic 'Association rule data mining'

Author: Grafiati
Published: 10 December 2022
Last updated: 29 January 2023

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Journal articles on the topic "Association rule data mining"

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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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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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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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Yatinkumar Kantilal, Solanki, and Yogesh Kumar Sharma. "UNDERSTANDING ASSOCIATION RULE IN DATA MINING." International Journal of Advanced Research 8, no. 6 (2020): 289–92. http://dx.doi.org/10.21474/ijar01/11097.

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Aly Abd Elaty, Amr, Rashed Salem, and Hatem Abdel Kader. "Efficient streaming data association rule mining." النشرة المعلوماتیة فی الحاسبات والمعلومات 1, no. 1 (2019): 35–41. http://dx.doi.org/10.21608/fcihib.2019.107515.

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Mohan, S. Vijayarani, and Tamilarasi Angamuthu. "Association Rule Hiding in Privacy Preserving Data Mining." International Journal of Information Security and Privacy 12, no. 3 (2018): 141–63. http://dx.doi.org/10.4018/ijisp.2018070108.

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This article describes how privacy preserving data mining has become one of the most important and interesting research directions in data mining. With the help of data mining techniques, people can extract hidden information and discover patterns and relationships between the data items. In most of the situations, the extracted knowledge contains sensitive information about individuals and organizations. Moreover, this sensitive information can be misused for various purposes which violate the individual's privacy. Association rules frequently predetermine significant target marketing information about a business. Significant association rules provide knowledge to the data miner as they effectively summarize the data, while uncovering any hidden relations among items that hold in the data. Association rule hiding techniques are used for protecting the knowledge extracted by the sensitive association rules during the process of association rule mining. Association rule hiding refers to the process of modifying the original database in such a way that certain sensitive association rules disappear without seriously affecting the data and the non-sensitive rules. In this article, two new hiding techniques are proposed namely hiding technique based on genetic algorithm (HGA) and dummy items creation (DIC) technique. Hiding technique based on genetic algorithm is used for hiding sensitive association rules and the dummy items creation technique hides the sensitive rules as well as it creates dummy items for the modified sensitive items. Experimental results show the performance of the proposed techniques.
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Wang, Hui. "Strategies for Sensitive Association Rule Hiding." Applied Mechanics and Materials 336-338 (July 2013): 2203–6. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.2203.

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Data mining technologies are used widely while the side effects it incurred are concerned so seriously. Privacy preserving data mining is so important for data and knowledge security during data mining applications. Association rule extracted from data mining is one kind of the most popular knowledge. It is challenging to hide sensitive association rules extracted by data mining process and make less affection on non-sensitive rules and the original database. In this work, we focus on specific association rule automatic hiding. Novel strategies are proposed which are based on increasing the support of the left hand and decreasing the support of the right hand. Quality measurements for sensitive association rules hiding are presented.
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Ding, Qin, and William Perrizo. "Support-Less Association Rule Mining Using Tuple Count Cube." Journal of Information & Knowledge Management 06, no. 04 (2007): 271–80. http://dx.doi.org/10.1142/s0219649207001846.

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Association rule mining is one of the important tasks in data mining and knowledge discovery (KDD). The traditional task of association rule mining is to find all the rules with high support and high confidence. In some applications, we are interested in finding high confidence rules even though the support may be low. This type of problem differs from the traditional association rule mining problem; hence, it is called support-less association rule mining. Existing algorithms for association rule mining, such as the Apriori algorithm, cannot be used efficiently for support-less association rule mining since those algorithms mostly rely on identifying frequent item-sets with high support. In this paper, we propose a new model to perform support-less association rule mining, i.e., to derive high confidence rules regardless of their support level. A vertical data structure, the Peano Count Tree (P-tree), is used in our model to represent all the information we need. Based on the P-tree structure, we build a special data cube, called the Tuple Count Cube (T-cube), to derive high confidence rules. Data cube operations, such as roll-up, on T-cube, provide efficient ways to calculate the count information needed for support-less association rule mining.
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Sonia M, Delphin, John Robinson P, and Sebastian Rajasekaran A. "Mining Efficient Fuzzy Bio-Statistical Rules for Association of Sandalwood in Pachaimalai Hills." International Journal of Agricultural and Environmental Information Systems 6, no. 2 (2015): 40–76. http://dx.doi.org/10.4018/ijaeis.2015040104.

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The integration of association rules and correlation rules with fuzzy logic can produce more abstract and flexible patterns for many real life problems, since many quantitative features in real world, especially surveying the frequency of plant association in any region is fuzzy in nature. This paper presents a modification of a previously reported algorithm for mining fuzzy association and correlation rules, defines the concept of fuzzy partial and semi-partial correlation rule mining, and presents an original algorithm for mining fuzzy data based on correlation rule mining. It adds a regression model to the procedure for mining fuzzy correlation rules in order to predict one data instance from contributing more than others. It also utilizes statistical analysis for the data and the experimental results show a very high utility of fuzzy association rules and fuzzy correlation rule mining in modeling plant association problems. The newly proposed algorithm is utilized for seeking close associations and relationships between a group of plant species clustering around Sandalwood in Pachaimalai hills, Eastern Ghats, Tamilnadu.
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Anisya. "Data Mining Prediction of Oil Palm Fruit." JAIA - Journal of Artificial Intelligence and Applications 1, no. 2 (2021): 07–12. http://dx.doi.org/10.33372/jaia.v1i2.707.

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The development of information technology today is very meaningful for all circles. Currently, information technology has become a necessity in everyday life. The use of information technology is proven to facilitate human performance. Where the number of suppliers that supply palm oil fruit every year will affect the activities of companies engaged in palm oil production. So that currently the company needs a decision-making strategy in the procurement of oil palm fruit. Data mining is a technology that is very useful to help companies find very important information from data centers. Data mining predicts trends and characteristics of business behavior which are very useful to support important decision making. One of the techniques the writer uses is the Association Rule technique. Association Rule is a data mining technique to find association rules between item combinations. Using the Association Rule technique will help companies predict which suppliers will supply palm fruit in the following year. Meanwhile, to predict the load does not use the association rule method but uses existing data analysis.
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Dissertations / Theses on the topic "Association rule data mining"

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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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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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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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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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Pray, Keith A. "Apriori Sets And Sequences: Mining Association Rules from Time Sequence Attributes." Link to electronic thesis, 2004. http://www.wpi.edu/Pubs/ETD/Available/etd-0506104-150831/.

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Thesis (M.S.) -- Worcester Polytechnic Institute.<br>Keywords: mining complex data; temporal association rules; computer system performance; stock market analysis; sleep disorder data. Includes bibliographical references (p. 79-85).
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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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Ahmed, Shakil. "Strategies for partitioning data in association rule mining." Thesis, University of Liverpool, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.415661.

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Shrestha, Anuj. "Association Rule Mining of Biological Field Data Sets." Thesis, North Dakota State University, 2017. https://hdl.handle.net/10365/28394.

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Association rule mining is an important data mining technique, yet, its use in association analysis of biological data sets has been limited. This mining technique was applied on two biological data sets, a genome and a damselfly data set. The raw data sets were pre-processed, and then association analysis was performed with various configurations. The pre-processing task involves minimizing the number of association attributes in genome data and creating the association attributes in damselfly data. The configurations include generation of single/maximal rules and handling single/multiple tier attributes. Both data sets have a binary class label and using association analysis, attributes of importance to each of these class labels are found. The results (rules) from association analysis are then visualized using graph networks by incorporating the association attributes like support and confidence, differential color schemes and features from the pre-processed data.<br>Bioinformatics Seed Grant Program NIH/UND<br>National Science Foundation (NSF) Grant IIA-1355466
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Rantzau, Ralf. "Extended concepts for association rule discovery." [S.l. : s.n.], 1997. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB8937694.

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Books on the topic "Association rule data mining"

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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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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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1978-, Koh Yun Sing, and Rountree Nathan 1974-, eds. Rare association rule mining and knowledge discovery: Technologies for infrequent and critical event detection. Information Science Reference, 2010.

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Data Mining for Association Rules and Sequential Patterns: Sequential and Parallel Algorithms. Springer New York, 2001.

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

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1969-, Li Xiao-Li, and Ng See-Kiong, eds. Biological data mining in protein interaction networks. Medical Information Science Reference, 2009.

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Kazienko, Przemysław. Associations: Discovery, analysis and applications. Oficyna Wydawnicza Politechniki Wrocławskiej, 2008.

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Book chapters on the topic "Association rule data mining"

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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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Bramer, Max. "Association Rule Mining I." In Principles of Data Mining. Springer London, 2020. http://dx.doi.org/10.1007/978-1-4471-7493-6_16.

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Bramer, Max. "Association Rule Mining II." In Principles of Data Mining. Springer London, 2020. http://dx.doi.org/10.1007/978-1-4471-7493-6_17.

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Bramer, Max. "Association Rule Mining I." In Principles of Data Mining. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4884-5_16.

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Bramer, Max. "Association Rule Mining II." In Principles of Data Mining. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4884-5_17.

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Toivonen, Hannu. "Association Rule." In Encyclopedia of Machine Learning and Data Mining. Springer US, 2017. http://dx.doi.org/10.1007/978-1-4899-7687-1_38.

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Tan, Ting-Feng, Qing-Guo Wang, Tian-He Phang, Xian Li, Jiangshuai Huang, and Dan Zhang. "Temporal Association Rule Mining." In Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23862-3_24.

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Nguyen, Loan T. T., Thang Mai, and Bay Vo. "High Utility Association Rule Mining." In Studies in Big Data. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-04921-8_6.

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Anand Hareendran, S., and S. S. Vinod Chandra. "Association Rule Mining in Healthcare Analytics." In Data Mining and Big Data. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61845-6_4.

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Conference papers on the topic "Association rule data mining"

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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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Olaru, Andrei, Claudia Marinica, and Fabrice Guillet. "Local mining of Association Rules with Rule Schemas." In 2009 IEEE Symposium on Computational Intelligence and Data Mining (CIDM). IEEE, 2009. http://dx.doi.org/10.1109/cidm.2009.4938638.

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Liu, Fang, Wee Keong Ng, and Wei Zhang. "Encrypted Association Rule Mining for Outsourced Data Mining." In 2015 IEEE 29th International Conference on Advanced Information Networking and Applications (AINA). IEEE, 2015. http://dx.doi.org/10.1109/aina.2015.235.

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Saito, Hidekazu, Akito Monden, and Zeynep Yucel. "Extended Association Rule Mining with Correlation Functions." In 2018 IEEE International Conference on Big Data, Cloud Computing, Data Science & Engineering (BCD). IEEE, 2018. http://dx.doi.org/10.1109/bcd2018.2018.00020.

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Davis, Warren L., Peter Schwarz, and Evimaria Terzi. "Finding representative association rules from large rule collections." In Proceedings of the 2009 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2009. http://dx.doi.org/10.1137/1.9781611972795.45.

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Kabir, Md Faisal, Simone A. Ludwig, and Abu Saleh Abdullah. "Rule Discovery from Breast Cancer Risk Factors using Association Rule Mining." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622028.

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Olmezogullari, Erdi, and Ismail Ari. "Online Association Rule Mining over Fast Data." In 2013 IEEE International Congress on Big Data (BigData Congress). IEEE, 2013. http://dx.doi.org/10.1109/bigdata.congress.2013.77.

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Qin, Kun, Zequn Guan, Deren Li, Xinzhou Wang, and Qizhi Xiao. "Association rule mining based on concept lattice." In MIPPR 2005 Geospatial Information, Data Mining, and Applications, edited by Jianya Gong, Qing Zhu, Yaolin Liu, and Shuliang Wang. SPIE, 2005. http://dx.doi.org/10.1117/12.650388.

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Chi, Xu, and Zhang Wen Fang. "Review of association rule mining algorithm in data mining." In 2011 IEEE 3rd International Conference on Communication Software and Networks (ICCSN). IEEE, 2011. http://dx.doi.org/10.1109/iccsn.2011.6014622.

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Vanamala, Sunitha, L. Padma Sree, and S. Durga Bhavani. "Rare association rule mining for data stream." In 2014 International Conference on Computer and Communications Technologies (ICCCT). IEEE, 2014. http://dx.doi.org/10.1109/iccct2.2014.7066696.

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Reports on the topic "Association rule data mining"

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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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Neyedley, K., J. J. Hanley, P. Mercier-Langevin, and M. Fayek. Ore mineralogy, pyrite chemistry, and S isotope systematics of magmatic-hydrothermal Au mineralization associated with the Mooshla Intrusive Complex (MIC), Doyon-Bousquet-LaRonde mining camp, Abitibi greenstone belt, Québec. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/328985.

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The Mooshla Intrusive Complex (MIC) is an Archean polyphase magmatic body located in the Doyon-Bousquet-LaRonde (DBL) mining camp of the Abitibi greenstone belt, Québec. The MIC is spatially associated with numerous gold (Au)-rich VMS, epizonal 'intrusion-related' Au-Cu vein systems, and shear zone-hosted (orogenic?) Au deposits. To elucidate genetic links between deposits and the MIC, mineralized samples from two of the epizonal 'intrusion-related' Au-Cu vein systems (Doyon and Grand Duc Au-Cu) have been characterized using a variety of analytical techniques. Preliminary results indicate gold (as electrum) from both deposits occurs relatively late in the systems as it is primarily observed along fractures in pyrite and gangue minerals. At Grand Duc gold appears to have formed syn- to post-crystallization relative to base metal sulphides (e.g. chalcopyrite, sphalerite, pyrrhotite), whereas base metal sulphides at Doyon are relatively rare. The accessory ore mineral assemblage at Doyon is relatively simple compared to Grand Duc, consisting of petzite (Ag3AuTe2), calaverite (AuTe2), and hessite (Ag2Te), while accessory ore minerals at Grand Duc are comprised of tellurobismuthite (Bi2Te3), volynskite (AgBiTe2), native Te, tsumoite (BiTe) or tetradymite (Bi2Te2S), altaite (PbTe), petzite, calaverite, and hessite. Pyrite trace element distribution maps from representative pyrite grains from Doyon and Grand Duc were collected and confirm petrographic observations that Au occurs relatively late. Pyrite from Doyon appears to have been initially trace-element poor, then became enriched in As, followed by the ore metal stage consisting of Au-Ag-Te-Bi-Pb-Cu enrichment and lastly a Co-Ni-Se(?) stage enrichment. Grand Duc pyrite is more complex with initial enrichments in Co-Se-As (Stage 1) followed by an increase in As-Co(?) concentrations (Stage 2). The ore metal stage (Stage 3) is indicated by another increase in As coupled with Au-Ag-Bi-Te-Sb-Pb-Ni-Cu-Zn-Sn-Cd-In enrichment. The final stage of pyrite growth (Stage 4) is represented by the same element assemblage as Stage 3 but at lower concentrations. Preliminary sulphur isotope data from Grand Duc indicates pyrite, pyrrhotite, and chalcopyrite all have similar delta-34S values (~1.5 � 1 permille) with no core-to-rim variations. Pyrite from Doyon has slightly higher delta-34S values (~2.5 � 1 permille) compared to Grand Duc but similarly does not show much core-to-rim variation. At Grand Duc, the occurrence of Au concentrating along the rim of pyrite grains and associated with an enrichment in As and other metals (Sb-Ag-Bi-Te) shares similarities with porphyry and epithermal deposits, and the overall metal association of Au with Te and Bi is a hallmark of other intrusion-related gold systems. The occurrence of the ore metal-rich rims on pyrite from Grand Duc could be related to fluid boiling which results in the destabilization of gold-bearing aqueous complexes. Pyrite from Doyon does not show this inferred boiling texture but shares characteristics of dissolution-reprecipitation processes, where metals in the pyrite lattice are dissolved and then reconcentrated into discrete mineral phases that commonly precipitate in voids and fractures created during pyrite dissolution.
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