Journal articles: '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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Lekha, A., C. V. Srikrishna, and Viji Vinod. "Fuzzy Association Rule Mining." Journal of Computer Science 11, no. 1 (2015): 71–74. http://dx.doi.org/10.3844/jcssp.2015.71.74.

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Hidber, Christian. "Online association rule mining." ACM SIGMOD Record 28, no. 2 (1999): 145–56. http://dx.doi.org/10.1145/304181.304195.

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Srikant, Ramakrishnan, and Rakesh Agrawal. "Mining generalized association rules." Future Generation Computer Systems 13, no. 2-3 (1997): 161–80. http://dx.doi.org/10.1016/s0167-739x(97)00019-8.

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Mani, Tushar. "Mining Negative Association Rules." IOSR Journal of Computer Engineering 3, no. 6 (2012): 43–47. http://dx.doi.org/10.9790/0661-0364347.

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Varma, Sandeep, and LijiP I. "Secure Outsourced Association Rule Mining using Homomorphic Encryption." International Journal of Engineering Research and Science 3, no. 9 (2017): 70–76. http://dx.doi.org/10.25125/engineering-journal-ijoer-sep-2017-22.

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G.Usha Rani, G. Usha Rani, R. Vijaya Prakash, and Prof A. Govardhan Prof. A. Govardhan. "Mining Multilevel Association Rule Using Pincer Search Algorithm." International Journal of Scientific Research 2, no. 5 (2012): 54–57. http://dx.doi.org/10.15373/22778179/may2013/21.

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Kazienko, Przemysław. "Mining Indirect Association Rules for Web Recommendation." International Journal of Applied Mathematics and Computer Science 19, no. 1 (2009): 165–86. http://dx.doi.org/10.2478/v10006-009-0015-5.

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Mining Indirect Association Rules for Web RecommendationClassical association rules, here called "direct", reflect relationships existing between items that relatively often co-occur in common transactions. In the web domain, items correspond to pages and transactions to user sessions. The main idea of the new approach presented is to discover indirect associations existing between pages that rarely occur together but there are other, "third" pages, called transitive, with which they appear relatively frequently. Two types of indirect associations rules are described in the paper: partial indirect associations and complete ones. The former respect single transitive pages, while the latter cover all existing transitive pages. The presented IDARM* Algorithm extracts complete indirect association rules with their important measure—confidence—using pre-calculated direct rules. Both direct and indirect rules are joined into one set of complex association rules, which may be used for the recommendation of web pages. Performed experiments revealed the usefulness of indirect rules for the extension of a typical recommendation list. They also deliver new knowledge not available to direct ones. The relation between ranking lists created on the basis of direct association rules as well as hyperlinks existing on web pages is also examined.

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Kaur, Simranjit, and Seema Baghla. "Data Mining Approach in Retail Knowledge Discovery and Internet Technologies." Asian Journal of Engineering and Applied Technology 7, no. 2 (2018): 100–105. http://dx.doi.org/10.51983/ajeat-2018.7.2.998.

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Online shopping has a shopping channel or purchasing various items through online medium. Data mining is defined as a process used to extract usable data from a larger set of any raw data. The data set extraction from the demographic profiles and Questionnaire to investigate the gathered based by association. The method for shopping was totally changed with the happening to internet Technology. Association rule mining is one of the important problems of data mining has been used here. The goal of the association rule mining is to detect relationships or associations between specific values of categorical variables in large data sets.

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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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Vo, Bay, Tam Tran, Tzung-Pei Hong, and Nguyen Minh. "Using Soft Set Theory for Mining Maximal Association Rules in Text Data." JUCS - Journal of Universal Computer Science 22, no. (6) (2016): 802–21. https://doi.org/10.3217/jucs-022-06-0802.

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Using soft set theory for mining maximal association rules based on the concept of frequent maximal itemsets which appear maximally in many records has been developed in recent years. This method has been shown to be very effective for mining interesting association rules which are not obtained by using methods for regular association rule mining. There have been several algorithms developed to solve the problem, but overall, they retain weaknesses related to the use of memory as well as mining time. In this paper, we propose an effective strategy for maximal rules mining based on soft set theory that consists of the following steps: 1) Build tree Max_IT_Tree where each node contains maximal itemsets X, the category of X, the set of transactions in which X is maximal, and the support of the maximal itemsets X for each category. 2) From the tree Max_IT_Tree built in previous steps, build a tree Max_Item_IT_Tree so that each maximal itemset has child nodes where each node contains items with categories different from the category of maximal itemsets. 3) Generate maximal association rules which satisfy predefined minimum M-support (min M-sup) and minimum M-confidence (min M-conf) thresholds.

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Xing, Zhengzheng, and Jian Pei. "Exploring Disease Association from the NHANES Data." International Journal of Data Warehousing and Mining 6, no. 3 (2010): 11–27. http://dx.doi.org/10.4018/jdwm.2010070102.

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Finding associations among different diseases is an important task in medical data mining. The NHANES data is a valuable source in exploring disease associations. However, existing studies analyzing the NHANES data focus on using statistical techniques to test a small number of hypotheses. This NHANES data has not been systematically explored for mining disease association patterns. In this regard, this paper proposes a direct disease pattern mining method and an interactive disease pattern mining method to explore the NHANES data. The results on the latest NHANES data demonstrate that these methods can mine meaningful disease associations consistent with the existing knowledge and literatures. Furthermore, this study provides summarization of the data set via a disease influence graph and a disease hierarchical tree.

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Nupur, Bhagoriya* Deepak Agrawal Zeba Qureshi. "TEMPORAL ASSOCIATION RULE MINING: A SURVEY IN FUZZY FRAMEWORK." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 4 (2017): 706–9. https://doi.org/10.5281/zenodo.569946.

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Temporal data mining generate temporal association rule that encapsulate transaction of item with time that’s recorded in temporal data base. Now these days recent research has focused to generate efficient fuzzy temporal association rule and transforming each quantitative value into fuzzy sets using the given membership functions. This paper presents a survey on temporal association rule and fuzzy logic. The Technical constraint of temporal data mining and fuzzy logic are identified and presented.

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Wang, Ching-Huan, Phung Anh Nguyen, Yu Chuan (Jack) Li, et al. "Improved diagnosis-medication association mining to reduce pseudo-associations." Computer Methods and Programs in Biomedicine 207 (August 2021): 106181. http://dx.doi.org/10.1016/j.cmpb.2021.106181.

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K., Rajendra Prasad. "Optimized High-Utility Itemsets Mining for Effective Association Mining Paper." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 5 (2017): 2911–18. https://doi.org/10.11591/ijece.v7i5.pp2911-2918.

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Association rule mining is intently used for determining the frequent itemsets of transactional database; however, it is needed to consider the utility of itemsets in market behavioral applications. Apriori or FP-growth methods generate the association rules without utility factor of items. High-utility itemset mining (HUIM) is a well-known method that effectively determines the itemsets based on high-utility value and the resulting itemsets are known as high-utility itemsets. Fastest high-utility mining method (FHM) is an enhanced version of HUIM. FHM reduces the number of join operations during itemsets generation, so it is faster than HUIM. For large datasets, both methods are very expenisve. Proposed method addressed this issue by building pruning based utility co-occurrence structure (PEUCS) for elimatination of low-profit itemsets, thus, obviously it process only optimal number of high-utility itemsets, so it is called as optimal FHM (OFHM). Experimental results show that OFHM takes less computational runtime, therefore it is more efficient

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Shatnawi, Raed, Qutaibah Althebyan, Baraq Ghaleb, and Mohammed Al-Maolegi. "A Student Advising System Using Association Rule Mining." International Journal of Web-Based Learning and Teaching Technologies 16, no. 3 (2021): 65–78. http://dx.doi.org/10.4018/ijwltt.20210501.oa5.

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Academic advising is a time-consuming activity that takes a considerable effort in guiding students to improve student performance. Traditional advising systems depend greatly on the effort of the advisor to find the best selection of courses to improve student performance in the next semester. There is a need to know the associations and patterns among course registration. Finding associations among courses can guide and direct students in selecting the appropriate courses that leads to performance improvement. In this paper, the authors propose to use association rule mining to help both students and advisors in selecting and prioritizing courses. Association rules find dependences among courses that help students in selecting courses based on their performance in previous courses. The association rule mining is conducted on thousands of student records to find associations between courses that have been registered by students in many previous semesters. The system has successfully generated a list of association rules that guide a particular student to select courses. The system was validated on the registration of 100 students, and the precision and recall showed acceptable prediction of courses.

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Tata, Gayathri, and Durga N. "Privacy Preserving Approaches for High Dimensional Data." International Journal of Trend in Scientific Research and Development 1, no. 5 (2017): 1120–25. https://doi.org/10.31142/ijtsrd2430.

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This paper proposes a model for hiding sensitive association rules for Privacy preserving in high dimensional data. Privacy preservation is a big challenge in data mining. The protection of sensitive information becomes a critical issue when releasing data to outside parties. Association rule mining could be very useful in such situations. It could be used to identify all the possible ways by which 'non confidential' data can reveal 'confidential' data, which is commonly known as 'inference problem'. This issue is solved using Association Rule Hiding ARH techniques in Privacy Preserving Data Mining PPDM . Association rule hiding aims to conceal these association rules so that no sensitive information can be mined from the database. Tata Gayathri | N Durga "Privacy Preserving Approaches for High Dimensional Data" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-5 , August 2017, URL: https://www.ijtsrd.com/papers/ijtsrd2430.pdf

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Tan, Jun, and Ying Yong Bu. "Association Rules Mining in Manufacturing." Applied Mechanics and Materials 34-35 (October 2010): 651–54. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.651.

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In recent years, manufacturing processes have become more and more complex, manufacturing activities generate large quantities of data, so it is no longer practical to rely on traditional manual methods to analyze this data. Data mining offers tools for extracting knowledge from data, leading to significant improvement in the decision-making process. Association rules mining is one of the most important data mining techniques and has received considerable attention from researchers and practitioners. The paper presents the basic concept of association rule mining and reviews applications of association rules in manufacturing, including product design, manufacturing, process, customer relationship management, supply chain management, and product quality improvement. This paper is focused on demonstrating the relevancy of association rules mining to manufacturing industry, rather than discussing the association rules mining domain in general.

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Kanimozhi Selvi, C. S., and A. Tamilarasi. "Mining Association rules with Dynamic and Collective Support Thresholds." International Journal of Engineering and Technology 1, no. 3 (2009): 236–40. http://dx.doi.org/10.7763/ijet.2009.v1.44.

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El, Mahjouby Mohamed, Bennani Mohamed Taj, Mohamed Lamrini, and Far Mohamed El. "Association rules forecasting for the foreign exchange market." Association rules forecasting for the foreign exchange market 14, no. 3 (2024): 3443–54. https://doi.org/10.11591/ijece.v14i3.pp3443-3454.

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Several association rule mining algorithms exist, and among them, Apriori is one of the most commonly used methods for extracting frequent item sets from vast databases and generating association rules to gain insights. In this research, we have applied a data mining technique to implement association rules and explore frequent item sets. Our study introduced a model that employs association rules to uncover associations between the foreign exchange market, the gold commodity, and the National Association of Securities Dealers automated quotations (NASDAQ). We suggested a method that used data mining to identify the good points of buying and selling in the foreign exchange market by utilizing technical indicators such as moving average convergence divergence (MACD) and the stochastic indicator to create association rules. The experimental findings indicate that the proposed model successfully generates strong association rules.

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Patil, Sonal, and Harshad Patil. "Secure Mining of Association Rules in Horizontally Distributed Databases." COMPUSOFT: An International Journal of Advanced Computer Technology 03, no. 03 (2014): 663–67. https://doi.org/10.5281/zenodo.14715540.

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We propose a protocol for secure mining of association rules in horizontally distributed databases. Our protocol is optimized than the Fast Distributed Mining (FDM) algorithm which is an unsecured distributed version of the Apriori algorithm. The main purpose of our protocol is to remove the problem of mining generalized association rules that affects the existing system. Our protocol offers more enhanced privacy with respect to previous protocols. In addition, it is simpler and is optimized in terms of communication rounds, communication cost and computational cost than other protocols . 

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EL Mahjouby, Mohamed, Mohamed Taj Bennani, Mohamed Lamrini, and Mohamed El Far. "Association rules forecasting for the foreign exchange market." International Journal of Electrical and Computer Engineering (IJECE) 14, no. 3 (2024): 3443. http://dx.doi.org/10.11591/ijece.v14i3.pp3443-3454.

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Several association rule mining algorithms exist, and among them, Apriori is one of the most commonly used methods for extracting frequent item sets from vast databases and generating association rules to gain insights. In this research, we have applied a data mining technique to implement association rules and explore frequent item sets. Our study introduced a model that employs association rules to uncover associations between the foreign exchange market, the gold commodity, and the National Association of Securities Dealers automated quotations (NASDAQ). We suggested a method that used data mining to identify the good points of buying and selling in the foreign exchange market by utilizing technical indicators such as moving average convergence divergence (MACD) and the stochastic indicator to create association rules. The experimental findings indicate that the proposed model successfully generates strong association rules.

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PriyankaV., Mahadik, and Kosbatwar Shyam P. "Mining Anomaly using Association Rule." International Journal of Computer Applications 67, no. 24 (2013): 9–12. http://dx.doi.org/10.5120/11734-7338.

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Yao, Ran Bo, An Ping Song, Xue Hai Ding, and Ming Bo Li. "Cross Sellingusing Association Rule Mining." Applied Mechanics and Materials 687-691 (November 2014): 1337–41. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.1337.

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In the retail enterprises, it is an important problem to choose goods group through their sales record.We should consider not only the direct benefits of product, but also the benefits bring by the cross selling. On the base of the mutual promotion in cross selling, in this paper we propose a new method to generate the optimal selected model. Firstly we use Apriori algorithm to obtain the frequent item sets and analyses the association rules sets between products.And then we analyses the above results to generate the optimal products mixes and recommend relationship in cross selling. The experimental result shows the proposed method has some practical value to the decisions of cross selling.

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Rajavat, Anand, and Pranjal singh solanki. "Modern Association Rule Mining Methods." International Journal of Computational Science and Information Technology 2, no. 4 (2014): 1–9. http://dx.doi.org/10.5121/ijcsity.2014.2401.

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Taha, Mohamed, Tarek F. Gharib, and Hamed Nassar. "DARM: Decremental Association Rules Mining." Journal of Intelligent Learning Systems and Applications 03, no. 03 (2011): 181–89. http://dx.doi.org/10.4236/jilsa.2011.33019.

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LIU, Xu-hui, Shi-huang SHAO, and Guang-zhu YU. "Motivation-based association rule mining." Journal of Computer Applications 29, no. 1 (2009): 189–92. http://dx.doi.org/10.3724/sp.j.1087.2009.00189.

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Liu, Fang, Zhengding Lu, and Songfeng Lu. "Mining association rules using clustering." Intelligent Data Analysis 5, no. 4 (2001): 309–26. http://dx.doi.org/10.3233/ida-2001-5403.

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Jin Soung Yoo and S. Shekhar. "Similarity-Profiled Temporal Association Mining." IEEE Transactions on Knowledge and Data Engineering 21, no. 8 (2009): 1147–61. http://dx.doi.org/10.1109/tkde.2008.185.

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Agrawal, R., and J. C. Shafer. "Parallel mining of association rules." IEEE Transactions on Knowledge and Data Engineering 8, no. 6 (1996): 962–69. http://dx.doi.org/10.1109/69.553164.

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Zaki, M. J. "Scalable algorithms for association mining." IEEE Transactions on Knowledge and Data Engineering 12, no. 3 (2000): 372–90. http://dx.doi.org/10.1109/69.846291.

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Zaki, Mohammed J. "Mining Non-Redundant Association Rules." Data Mining and Knowledge Discovery 9, no. 3 (2004): 223–48. http://dx.doi.org/10.1023/b:dami.0000040429.96086.c7.

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Nanopoulos, Alexandros, and Yannis Manolopoulos. "Memory-adaptive association rules mining." Information Systems 29, no. 5 (2004): 365–84. http://dx.doi.org/10.1016/s0306-4379(03)00035-8.

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Anand, H. S., and S. S. Vinodchandra. "Association rule mining using treap." International Journal of Machine Learning and Cybernetics 9, no. 4 (2016): 589–97. http://dx.doi.org/10.1007/s13042-016-0546-7.

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Chiang, Ding-An, Yi-Fan Wang, Yi-Hsin Wang, Zhi-Yang Chen, and Mei-Hua Hsu. "Mining disjunctive consequent association rules." Applied Soft Computing 11, no. 2 (2011): 2129–33. http://dx.doi.org/10.1016/j.asoc.2010.07.011.

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Kaur, Jagmeet, and Neena Madan. "Association Rule Mining: A Survey." International Journal of Hybrid Information Technology 8, no. 7 (2015): 239–42. http://dx.doi.org/10.14257/ijhit.2015.8.7.22.

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YILDIRIM TAŞER, Pelin, Kökten Ulaş BİRANT, and Derya BİRANT. "Multitask-based association rule mining." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 28, no. 2 (2020): 933–55. http://dx.doi.org/10.3906/elk-1905-88.

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Taniar, David, Wenny Rahayu, Olena Daly, and Hong-Quang Nguyen. "Mining Hierarchical Negative Association Rules." International Journal of Computational Intelligence Systems 5, no. 3 (2012): 434–51. http://dx.doi.org/10.1080/18756891.2012.696905.

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Subramanyam, R. B. V., and A. Goswami. "Mining fuzzy quantitative association rules." Expert Systems 23, no. 4 (2006): 212–25. http://dx.doi.org/10.1111/j.1468-0394.2006.00402.x.

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Lee, Wan-Jui, Jung-Yi Jiang, and Shie-Jue Lee. "Mining fuzzy periodic association rules." Data & Knowledge Engineering 65, no. 3 (2008): 442–62. http://dx.doi.org/10.1016/j.datak.2007.11.002.

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Gandhimathi, D., and N. Anbazhagan. "Extracting of Positive and Negative Association Rules." International Journal of Emerging Research in Management and Technology 6, no. 8 (2018): 421. http://dx.doi.org/10.23956/ijermt.v6i8.175.

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Abstract:

Association rules analysis is a basic technique to expose how items/patterns are associated to each other. There are two common ways to measure association such as Support and Confidence. Several methods have been proposed in the literature to diminish the number of extracted association rules. Association Rule Mining is one of the greatest current data mining techniques designed to group objects together from huge databases aiming to take out the motivating correlation and relation with massive quantity of data. Association rule mining is used to discover the associated patterns from datasets. In this paper, we propose association rules from new methods on web usage mining. Generally, web usage log structure has several records so we have to overcome those unwanted records from large dataset. First of all the pre-processed data from the NASA dataset is clustered by the popular K-Means algorithm. Subsequently, the matrix calculation is progressed on that data. Further, the associations are performed on filtered data and get rid of the final associated page results. Positive and negative association rules are gathered by using new algorithm with Annul Object (𝒜𝒪). Wherever the object “𝒜𝒪” is presented those rules are known as negative association rule. Otherwise, the rules are positive association rules.

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Journal articles on the topic 'Association Mining'

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