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Academic literature on the topic 'Weighted Association Rule Mining'

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Published: 14 March 2023

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Journal articles on the topic "Weighted Association Rule 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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Lin, Lin, and Mei-Ling Shyu. "Weighted Association Rule Mining for Video Semantic Detection." International Journal of Multimedia Data Engineering and Management 1, no. 1 (January 2010): 37–54. http://dx.doi.org/10.4018/jmdem.2010111203.

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Semantic knowledge detection of multimedia content has become a very popular research topic in recent years. The association rule mining (ARM) technique has been shown to be an efficient and accurate approach for content-based multimedia retrieval and semantic concept detection in many applications. To further improve the performance of traditional association rule mining technique, a video semantic concept detection framework whose classifier is built upon a new weighted association rule mining (WARM) algorithm is proposed in this article. Our proposed WARM algorithm is able to capture the different significance degrees of the items (feature-value pairs) in generating the association rules for video semantic concept detection. Our proposed WARM-based framework first applies multiple correspondence analysis (MCA) to project the features and classes into a new principle component space and discover the correlation between feature-value pairs and classes. Next, it considers both correlation and percentage information as the measurement to weight the feature-value pairs and to generate the association rules. Finally, it performs classification by using these weighted association rules. To evaluate our WARM-based framework, we compare its performance of video semantic concept detection with several well-known classifiers using the benchmark data available from the 2007 and 2008 TRECVID projects. The results demonstrate that our WARM-based framework achieves promising performance and performs significantly better than those classifiers in the comparison.
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Qu, Zhi Cheng, Meng Ye, and Bin Jiang. "Mining Method for Weighted Concise Association Rules Based on Closed Itemsets under Weighted Support Framework." Applied Mechanics and Materials 236-237 (November 2012): 326–33. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.326.

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Association rules tell us interesting relationships between different items in transaction database. But traditional association rule has two disadvantages. Firstly it assumes every two items have same significance in database, which is unreasonable in many real applications and usually leads to incorrect results. On the other hand, traditional association rule representation contains too much redundancy which makes it difficult to be mined and used. This paper addresses the problem of mining weighted concise association rules based on closed itemsets under weighted support-significant framework, in which each item with different significance is assigned different weight. Through exploiting specific technique, the proposed algorithm can mine all weighted concise association rules while duplicate weighted itemset search space is pruned. As illustrated in experiments, the proposed method leads to good results and achieves good performance.
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Lu, Songfeng, Heping Hu, and Fan Li. "Mining weighted association rules." Intelligent Data Analysis 5, no. 3 (May 1, 2001): 211–25. http://dx.doi.org/10.3233/ida-2001-5303.

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Tan, Jun. "Different Types of Association Rules Mining Review." Applied Mechanics and Materials 241-244 (December 2012): 1589–92. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1589.

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In recent years, many application systems have generate large quantities of data, so it is no longer practical to rely on traditional database technique to analyze these 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 technology. The paper first presents the basic concept of association rule mining, then discuss a few different types of association rules mining including multi-level association rules, multidimensional association rules, weighted association rules, multi-relational association rules, fuzzy association rules.
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Trivedi, Dhwaneel, Suraj Singh, and Rashmi Thakur. "Enhancement of Marketing Strategies using Weighted Association Rule Mining." International Journal of Computer Applications 68, no. 21 (April 18, 2013): 28–33. http://dx.doi.org/10.5120/11704-7311.

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Syed Ibrahim and Chandran. "Compact Weighted Class Association Rule Mining Using Information Gain." International Journal of Data Mining & Knowledge Management Process 1, no. 6 (November 30, 2011): 1–13. http://dx.doi.org/10.5121/ijdkp.2011.1601.

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Sivasakthi, Varuna. "Generation of Adverse Drug Event Detection Rules by Weighted Association Rule Mining." IOSR Journal of Engineering 3, no. 01 (January 2013): 43–46. http://dx.doi.org/10.9790/3021-03144346.

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KORPIPÄÄ, PANU. "Visualizing constraint-based temporal association rules." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 15, no. 5 (November 2001): 401–10. http://dx.doi.org/10.1017/s0890060401155034.

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When dealing with time continuous processes, the discovered association rules may change significantly over time. This often reflects a change in the process as well. Therefore, two questions arise: What kind of deviation occurs in the association rules over time, and how could these temporal rules be presented efficiently? To address this problem of representation, we propose a method of visualizing temporal association rules in a virtual model with interactive exploration. The presentation form is a three-dimensional correlation matrix, and the visualization methods used are brushing and glyphs. Interactive functions used for displaying rule attributes and exploring temporal rules are implemented by utilizing Virtual Reality Modeling Language v2 mechanisms. Furthermore, to give a direction of rule potential for the user, the rule statistical interestingness is evaluated on the basis of combining weighted characteristics of rule and rule matrix. A constraint-based association rule mining tool which creates the virtual model as an output is presented, including the most relevant experiences from the development of the tool. The applicability of the overall approach has been verified by using the developed tool for data mining on a hot strip mill of a steel plant.
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Mallik, Saurav, Anirban Mukhopadhyay, and Ujjwal Maulik. "Integrated Statistical and Rule-Mining Techniques for Dna Methylation and Gene Expression Data Analysis." Journal of Artificial Intelligence and Soft Computing Research 3, no. 2 (April 1, 2013): 101–15. http://dx.doi.org/10.2478/jaiscr-2014-0008.

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Abstract For determination of the relationships among significant gene markers, statistical analysis and association rule mining are considered as very useful protocols. The first protocol identifies the significant differentially expressed/methylated gene markers, whereas the second one produces the interesting relationships among them across different types of samples or conditions. In this article, statistical tests and association rule mining based approaches have been used on gene expression and DNA methylation datasets for the prediction of different classes of samples (viz., Uterine Leiomyoma/class-formersmoker and uterine myometrium/class-neversmoker). A novel rule-based classifier is proposed for this purpose. Depending on sixteen different rule-interestingness measures, we have utilized a Genetic Algorithm based rank aggregation technique on the association rules which are generated from the training set of data by Apriori association rule mining algorithm. After determining the ranks of the rules, we have conducted a majority voting technique on each test point to estimate its class-label through weighted-sum method. We have run this classifier on the combined dataset using 4-fold cross-validations, and thereafter a comparative performance analysis has been made with other popular rulebased classifiers. Finally, the status of some important gene markers has been identified through the frequency analysis in the evolved rules for the two class-labels individually to formulate the interesting associations among them.
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Dissertations / Theses on the topic "Weighted Association Rule Mining"

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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.
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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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.
Keywords: Itemset Pruning, Association Rules, Adaptive Minimal Support, Associative Classification, Classification. Includes bibliographical references (p.70-74).
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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.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Information and Communication Technology
Faculty of Engineering and Information Technology
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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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Lin, Weiyang. "Association rule mining for collaborative recommender systems." Link to electronic version, 2000. http://www.wpi.edu/Pubs/ETD/Available/etd-0515100-145926.

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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 "Weighted Association Rule Mining"

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

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Gkoulalas-Divanis, Aris, and Vassilios S. Verykios. Association Rule Hiding for Data Mining. Boston, MA: 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. New York: Springer, 2010.

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

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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. Hershey, PA: Information Science Reference, 2010.

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

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Zhang, Chengqi, and Shichao Zhang. Association Rule Mining: Models and Algorithms. Springer London, Limited, 2006.

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Gkoulalas-Divanis, Aris, and Vassilios S. Verykios. Association Rule Hiding for Data Mining. Springer, 2012.

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Association Rule Mining: Models and Algorithms (Lecture Notes in Computer Science). Springer, 2002.

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Assoziationsregel-Algorithmen Fur Daten Mit Komplexer Struktur: Mit Anwendungen Im Web Mining (Informationstechnologie Und Okonomie). Peter Lang Publishing, 2003.

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Book chapters on the topic "Weighted Association Rule Mining"

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Koh, Yun Sing, Russel Pears, and Wai Yeap. "Valency Based Weighted Association Rule Mining." In Advances in Knowledge Discovery and Data Mining, 274–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13657-3_31.

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Koh, Yun Sing, Russel Pears, and Gillian Dobbie. "WeightTransmitter: Weighted Association Rule Mining Using Landmark Weights." In Advances in Knowledge Discovery and Data Mining, 37–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-30220-6_4.

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Pears, Russel, and Yun Sing Koh. "Weighted Association Rule Mining Using Particle Swarm Optimization." In New Frontiers in Applied Data Mining, 327–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28320-8_28.

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Muyeba, Maybin, M. Sulaiman Khan, and Frans Coenen. "Fuzzy Weighted Association Rule Mining with Weighted Support and Confidence Framework." In New Frontiers in Applied Data Mining, 49–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00399-8_5.

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Pears, Russel, Yun Sing Koh, and Gillian Dobbie. "EWGen: Automatic Generation of Item Weights for Weighted Association Rule Mining." In Advanced Data Mining and Applications, 36–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17316-5_4.

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Kaya, Mehmet, and Reda Alhajj. "Online Mining of Weighted Fuzzy Association Rules." In Computer and Information Sciences - ISCIS 2003, 308–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39737-3_39.

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Yan, Zhijun, Kai Liu, Meiming Xing, Tianmei Wang, and Baowen Sun. "A Study of Complication Identification Based on Weighted Association Rule Mining." In Socially Aware Organisations and Technologies. Impact and Challenges, 149–58. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42102-5_17.

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Gyenesei, Attila. "Mining Weighted Association Rules for Fuzzy Quantitative Items." In Principles of Data Mining and Knowledge Discovery, 416–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45372-5_45.

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Liu, Jing, Qingxiang Zhu, Haipeng Ji, Honghong Hao, and Pengsha Jiao. "Weighted Association Rule for Mining-Based Graphic Processing Unit for Fault Diagnosis." In 2011 International Conference in Electrics, Communication and Automatic Control Proceedings, 251–56. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-8849-2_32.

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Abdullah, Zailani, Tutut Herawan, and Mustafa Mat Deris. "An Alternative Measure for Mining Weighted Least Association Rule and Its Framework." In Software Engineering and Computer Systems, 480–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22191-0_42.

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Conference papers on the topic "Weighted Association Rule Mining"

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Yue, Zhai, Wang Lijuan, and Wang Ning. "Efficient weighted association rule mining using lattice." In 2014 26th Chinese Control And Decision Conference (CCDC). IEEE, 2014. http://dx.doi.org/10.1109/ccdc.2014.6853053.

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Cengiz, Ayse Betul, Kokten Ulas Birant, and Derya Birant. "Analysis of Pre-Weighted and Post-Weighted Association Rule Mining." In 2019 Innovations in Intelligent Systems and Applications Conference (ASYU). IEEE, 2019. http://dx.doi.org/10.1109/asyu48272.2019.8946378.

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Tao, Feng, Fionn Murtagh, and Mohsen Farid. "Weighted Association Rule Mining using weighted support and significance framework." In the ninth ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/956750.956836.

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Olson, David L., and Yanhong Li. "Mining Fuzzy Weighted Association Rules." In Proceedings of the 40th Annual Hawaii International Conference on System Sciences. IEEE, 2007. http://dx.doi.org/10.1109/hicss.2007.341.

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Bose, Subrata, and Subrata Datta. "Frequent pattern generation in association rule mining using weighted support." In 2015 3rd International Conference on Computer, Communication, Control and Information Technology (C3IT). IEEE, 2015. http://dx.doi.org/10.1109/c3it.2015.7060207.

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Kumar, Preetham, and V. S. Ananthanarayana. "Discovery of weighted association rules mining." In 2nd International Conference on Computer and Automation Engineering (ICCAE 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccae.2010.5451339.

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Kumar, G. Praveen, and Anirban Sarkar. "Weighted Association Rule Mining and Clustering in Non-binary Search Space." In 2010 Seventh International Conference on Information Technology: New Generations. IEEE, 2010. http://dx.doi.org/10.1109/itng.2010.136.

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Kun-Ming Yu and Jia-Ling Zhou. "A weighted load-balancing parallel Apriori algorithm for association rule mining." In 2008 IEEE International Conference on Granular Computing (GrC-2008). IEEE, 2008. http://dx.doi.org/10.1109/grc.2008.4664768.

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Kharya, Shweta, Sunita Soni, and Tripti Swarnkar. "Weighted Bayesian Association Rule Mining Algorithm to Construct Bayesian Belief Network." In 2019 International Conference on Applied Machine Learning (ICAML). IEEE, 2019. http://dx.doi.org/10.1109/icaml48257.2019.00013.

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Wen, Chen. "Mining weighted association rules based on weighted Fp tree." In 2011 International Conference on E-Business and E-Government (ICEE). IEEE, 2011. http://dx.doi.org/10.1109/icebeg.2011.5884502.

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