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Journal articles on the topic 'Weighted Association Rule Mining'

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Author: Grafiati

Published: 14 March 2023

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1

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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5

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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9

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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Koh, Yun Sing, Russel Pears, and Gillian Dobbie. "Automatic Item Weight Generation for Pattern Mining and its Application." International Journal of Data Warehousing and Mining 7, no. 3 (July 2011): 30–49. http://dx.doi.org/10.4018/jdwm.2011070102.

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Association rule mining discovers relationships among items in a transactional database. Most approaches assume that all items within a dataset have a uniform distribution with respect to support. However, this is not always the case, and weighted association rule mining (WARM) was introduced to provide importance to individual items. Previous approaches to the weighted association rule mining problem require users to assign weights to items. In certain cases, it is difficult to provide weights to all items within a dataset. In this paper, the authors propose a method that is based on a novel Valency model that automatically infers item weights based on interactions between items. The authors experiment shows that the weighting scheme results in rules that better capture the natural variation that occurs in a dataset when compared with a miner that does not employ a weighting scheme. The authors applied the model in a real world application to mine text from a given collection of documents. The use of item weighting enabled the authors to attach more importance to terms that are distinctive. The results demonstrate that keyword discrimination via item weighting leads to informative rules.

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Arul Mary, S. A. Sahaaya, and Malarvizhi M. "Integrated Web Recommendation Model with Improved Weighted Association Rule Mining." International Journal of Data Mining & Knowledge Management Process 3, no. 2 (March 31, 2013): 87–105. http://dx.doi.org/10.5121/ijdkp.2013.3206.

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Pears, Russel, Yun Sing Koh, Gillian Dobbie, and Wai Yeap. "Weighted association rule mining via a graph based connectivity model." Information Sciences 218 (January 2013): 61–84. http://dx.doi.org/10.1016/j.ins.2012.07.001.

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Tan, Jun. "Weighted Association Rules Mining Algorithm Research." Applied Mechanics and Materials 241-244 (December 2012): 1598–601. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1598.

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Aiming at the problem that most of weighted association rules mining algorithms have not the anti-monotonicity, this paper presents a weighted support-confidence framework which supports anti-monotonicity. On this basis, weighted boolean association rules mining algorithm and weighted fuzzy association rules mining algorithm are presented, which use pruning strategy of Apriori algorithm so that improve the efficiency of frequent itemsets generated. Experimental results show that both algorithms have good performance.

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Gayathri, K. R. Snehaa. "Analyzing the Coronary Heart Events Based on Weighted Association Rule Mining." IOSR journal of VLSI and Signal Processing 1, no. 5 (2013): 12–16. http://dx.doi.org/10.9790/4200-0151216.

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Shamugasundaram, P., and N. S. Nithya. "An Analysis of Fuzzy Weighted Association Rule Mining from Medical Data." Asian Journal of Research in Social Sciences and Humanities 6, no. 7 (2016): 471. http://dx.doi.org/10.5958/2249-7315.2016.00439.1.

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He Jiang, Jinyong Cheng, Wenqing Lei, and Xiumei Luan. "Study on Incremental Updating Algorithm for Mining Weighted Negative Association Rule." International Journal of Advancements in Computing Technology 4, no. 19 (October 31, 2012): 491–98. http://dx.doi.org/10.4156/ijact.vol4.issue19.58.

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Shao, Yuanxun, Bin Liu, Shihai Wang, and Guoqi Li. "Software defect prediction based on correlation weighted class association rule mining." Knowledge-Based Systems 196 (May 2020): 105742. http://dx.doi.org/10.1016/j.knosys.2020.105742.

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Malarvizhi, S. P., and B. Sathiyabhama. "Frequent pagesets from web log by enhanced weighted association rule mining." Cluster Computing 19, no. 1 (November 21, 2015): 269–77. http://dx.doi.org/10.1007/s10586-015-0507-z.

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Bansal, Meenakshi, Dinesh Grover, and Dhiraj Sharma. "Sensitivity Association Rule Mining using Weight based Fuzzy Logic." Global Journal of Enterprise Information System 9, no. 2 (June 28, 2017): 1. http://dx.doi.org/10.18311/gjeis/2017/15480.

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Mining of sensitive rules is the most important task in data mining. Most of the existing techniques worked on finding sensitive rules based upon the crisp thresh hold value of support and confidence which cause serious side effects to the original database. To avoid these crisp boundaries this paper aims to use WFPPM (Weighted Fuzzy Privacy Preserving Mining) to extract sensitive association rules. WFPPM completely find the sensitive rules by calculating the weights of the rules. At first, we apply FP-Growth to mine association rules from the database. Next, we implement fuzzy to find the sensitive rules among the extracted rules. Experimental results show that the proposed scheme find actual sensitive rules without any modification along with maintaining the quality of the released data as compared to the previous techniques.

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Peng, Na Xin. "An Efficient Weighted Association Rules Mining Algorithm." Applied Mechanics and Materials 333-335 (July 2013): 1247–50. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1247.

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Aiming at the problem that most of weighted association rules algorithm have not the anti-monotonicity, this paper presents a weighted support-confidence framework which supports anti-monotonicity. On this basis, Boolean weighted association rules algorithm and weighted fuzzy association rules algorithm are presented, which use pruning strategy of Apriori algorithm so as to improve the efficiency of frequent itemsets generated. Experimental results show that both algorithms have good performance.

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Sun, K., and Fengshan Bai. "Mining Weighted Association Rules without Preassigned Weights." IEEE Transactions on Knowledge and Data Engineering 20, no. 4 (April 2008): 489–95. http://dx.doi.org/10.1109/tkde.2007.190723.

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23

Indhumathy, Murugan, Ahmed R. Nabhan, and Subramanian Arumugam. "A Weighted Association Rule Mining Method for Predicting HCV-Human Protein Interactions." Current Bioinformatics 13, no. 1 (February 19, 2018): 73–84. http://dx.doi.org/10.2174/1574893611666161123142425.

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K, Mangayarkkarasi, and Chidambaram M. "F-PNWAR: Fuzzy-based Positive and Negative Weighted Association Rule Mining Algorithm." International Journal of Engineering and Technology 9, no. 6 (December 31, 2017): 4250–57. http://dx.doi.org/10.21817/ijet/2017/v9i6/170906111.

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Weng, Cheng-Hsiung, and Tony Cheng-Kui Huang. "Knowledge acquisition of association rules from the customer-lifetime-value perspective." Kybernetes 47, no. 3 (March 5, 2018): 441–57. http://dx.doi.org/10.1108/k-03-2016-0042.

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Purpose Customer lifetime value (CLV) scoring is highly effective when applied to marketing databases. Some researchers have extended the traditional association rule problem by associating a weight with each item in a transaction. However, studies of association rule mining have considered the relative benefits or significance of “items” rather than “transactions” belonging to different customers. Because not all customers are financially attractive to firms, it is crucial that their profitability be determined and that transactions be weighted according to CLV. This study aims to discover association rules from the CLV perspective. Design/methodology/approach This study extended the traditional association rule problem by allowing the association of CLV weight with a transaction to reflect the interest and intensity of customer values. Furthermore, the authors proposed a new algorithm, frequent itemsets of CLV weight (FICLV), to discover frequent itemsets from CLV-weighted transactions. Findings Experimental results from the survey data indicate that the proposed FICLV algorithm can discover valuable frequent itemsets. Moreover, the frequent itemsets identified using the FICLV algorithm outperform those discovered through conventional approaches for predicting customer purchasing itemsets in the coming period. Originality/value This study is the first to introduce the optimum approach for discovering frequent itemsets from transactions through considering CLV.

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Aribowo, Agus Sasmito, and Nur Heri Cahyana. "Feasibility study for banking loan using association rule mining classifier." International Journal of Advances in Intelligent Informatics 1, no. 1 (March 31, 2015): 41. http://dx.doi.org/10.26555/ijain.v1i1.8.

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The problem of bad loans in the koperasi can be reduced if the koperasi can detect whether member can complete the mortgage debt or decline. The method used for identify characteristic patterns of prospective lenders in this study, called Association Rule Mining Classifier. Pattern of credit member will be converted into knowledge and used to classify other creditors. Classification process would separate creditors into two groups: good credit and bad credit groups. Research using prototyping for implementing the design into an application using programming language and development tool. The process of association rule mining using Weighted Itemset Tidset (WIT)–tree methods. The results shown that the method can predict the prospective customer credit. Training data set using 120 customers who already know their credit history. Data test used 61 customers who apply for credit. The results concluded that 42 customers will be paying off their loans and 19 clients are decline

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Subramanyam, R. B. V., and A. Goswami. "Mining Frequent Fuzzy Grids in Dynamic Databases with Weighted Transactions and Weighted Items." Journal of Information & Knowledge Management 05, no. 03 (September 2006): 243–57. http://dx.doi.org/10.1142/s0219649206001487.

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Incremental mining algorithms that derive the latest mining output by making use of previous mining results are attractive to business organisations. In this paper, a fuzzy data mining algorithm for incremental mining of frequent fuzzy grids from quantitative dynamic databases is proposed. It extends the traditional association rule problem by allowing a weight to be associated with each item in a transaction and with each transaction in a database to reflect the interest/intensity of items and transactions. It uses the information about fuzzy grids that are already mined from original database and avoids start-from-scratch process. In addition, we deal with "weights-of-significance" which are automatically regulated as the incremental databases are evolved and implant themselves in the original database. We maintain "hopeful fuzzy grids" and "frequent fuzzy grids" and our algorithm changes the status of the grids which have been discovered earlier so that they reflect the pattern drift in the updated quantitative databases. Our heuristic approach avoids maintaining many "hopeful fuzzy grids" at the initial level. The algorithm is illustrated with one numerical example and demonstration of experimental results are also incorporated.

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Kaya, Mehmet, and Reda Alhajj. "Online mining of fuzzy multidimensional weighted association rules." Applied Intelligence 29, no. 1 (September 26, 2007): 13–34. http://dx.doi.org/10.1007/s10489-007-0078-7.

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Malarvizhi, SP. "Recent and Frequent Informative Pages from Web Logs by Weighted Association Rule Mining." International Journal of Modern Education and Computer Science 11, no. 10 (October 8, 2019): 41–46. http://dx.doi.org/10.5815/ijmecs.2019.10.05.

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Mallik, Saurav, Anirban Mukhopadhyay, and Ujjwal Maulik. "RANWAR: Rank-Based Weighted Association Rule Mining From Gene Expression and Methylation Data." IEEE Transactions on NanoBioscience 14, no. 1 (January 2015): 59–66. http://dx.doi.org/10.1109/tnb.2014.2359494.

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Bhavithra, J., and A. Saradha. "Personalized web page recommendation using case-based clustering and weighted association rule mining." Cluster Computing 22, S3 (February 24, 2018): 6991–7002. http://dx.doi.org/10.1007/s10586-018-2053-y.

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NITHYA, N. S., and K. DURAISWAMY. "Gain ratio based fuzzy weighted association rule mining classifier for medical diagnostic interface." Sadhana 39, no. 1 (February 2014): 39–52. http://dx.doi.org/10.1007/s12046-013-0198-1.

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Cai, Jiang-Hui, Xu-Jun Zhao, Shi-Wei Sun, Ji-Fu Zhang, and Hai-Feng Yang. "Stellar spectra association rule mining method based on the weighted frequent pattern tree." Research in Astronomy and Astrophysics 13, no. 3 (March 2013): 334–42. http://dx.doi.org/10.1088/1674-4527/13/3/008.

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Altuntas, Serkan, and Hasan Selim. "Facility layout using weighted association rule-based data mining algorithms: Evaluation with simulation." Expert Systems with Applications 39, no. 1 (January 2012): 3–13. http://dx.doi.org/10.1016/j.eswa.2011.06.045.

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J. Serin, J. Serin, J. SatheeshKumar J. Serin, and T. Amudha J. SatheeshKumar. "Efficient Fuzzy C-means Based Reduced Feature Set Association Rule Mining Approach for Predicting the User Behavioral Pattern in Web Usage Mining." 網際網路技術學刊 23, no. 7 (December 2022): 1495–503. http://dx.doi.org/10.53106/160792642022122307005.

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<p>Online business and marketing are becoming popular now a day due to the wide variety of products available from multiple vendors in online. One of the major challenges of e-business merchants is predicting the buying and selling patterns of online customers. Global level competition is another challenge faced by online merchants due to the lowest prices and offers provided by multiple sellers for the same or similar product. Hence, the development of an efficient web mining framework to analyze and predict buyer’s interest based on the browsing history will be a great support to the online sellers by providing exact or relevant product details to the buyers in online. Association rule mining plays an essential role in Web Mining for finding the most frequent and predictive patterns of the user. The major challenge in this approach is the generation of many rules for a huge volume of datasets. Decision making based on association rule mining is critical because knowledge is not directly present in frequent patterns. This research work focuses on the analysis of standard web mining approaches such as k-means clustering, fuzzy c-means clustering, fuzzy k-medoids clustering and fuzzy clustering with weighted session page matrix approach. In this work, MSNBC dataset from UCI Machine Learning Repository has been taken for analysis. Dimensionality reduction plays an important role in the accurate classification of users with respect to their interests. This research work proposed fuzzy C-Means using Kernel Principal Component Analysis (k-PCA) as a dimensionality reduction method based association rule mining classification, grouping and pattern prediction with 100% “confidence” along with a “lift” value greater than 1. The “support” value also shows higher compare with other existing methods and features are effectively reduced in the proposed architecture.</p> <p> </p>

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Soni, Sunita, and O. P. Vyas. "Building Weighted Associative Classifiers using Maximum Likelihood Estimation to Improve Prediction Accuracy in Health Care Data Mining." Journal of Information & Knowledge Management 12, no. 01 (March 2013): 1350008. http://dx.doi.org/10.1142/s0219649213500081.

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Associative classifiers are new classification approach that use association rules for classification. An important advantage of these classification systems is that, using association rule mining (ARM) they are able to examine several features at a time. Many applications can benefit from good classification model. Associative classifiers are especially fit to applications where the model may assist the domain experts in their decisions. Medical diagnosis is a domain where the maximum accuracy of the model is desired. In this paper, we propose a framework weighted associative classifier (WAC) that assigns different weights to different attributes according to their predicting capability. We are using maximum likelihood estimation (MLE) theory to calculate weight of each attribute using training data. We also show how existing Apriori algorithm can be modified in weighted environment to infer association rule from medical dataset having numeric valued attributes as the conventional ARM usually deals with the transaction database with categorical values. Experiments have been performed on benchmark data set to evaluate the performance of WAC in terms of accuracy, number of rules generating and impact of minimum support threshold on WAC outcomes. The result reveals that WAC is a promising alternative in medical prediction and certainly deserves further attention.

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Lin, Lin, Mei-Ling Shyu, and Shu-Ching Chen. "Rule-Based Semantic Concept Classification from Large-Scale Video Collections." International Journal of Multimedia Data Engineering and Management 4, no. 1 (January 2013): 46–67. http://dx.doi.org/10.4018/jmdem.2013010103.

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The explosive growth and increasing complexity of the multimedia data have created a high demand of multimedia services and applications in various areas so that people can access and distribute the data easily. Unfortunately, traditional keyword-based information retrieval is no longer suitable. Instead, multimedia data mining and content-based multimedia information retrieval have become the key technologies in modern societies. Among many data mining techniques, association rule mining (ARM) is considered one of the most popular approaches to extract useful information from multimedia data in terms of relationships between variables. In this paper, a novel rule-based semantic concept classification framework using weighted association rule mining (WARM), capturing the significance degrees of the feature-value pairs to improve the applicability of ARM, is proposed to deal with major issues and challenges in large-scale video semantic concept classification. Unlike traditional ARM that the rules are generated by frequency count and the items existing in one rule are equally important, our proposed WARM algorithm utilizes multiple correspondence analysis (MCA) to explore the relationships among features and concepts and to signify different contributions of the features in rule generation. To the authors best knowledge, this is one of the first WARM-based classifiers in the field of multimedia concept retrieval. The experimental results on the benchmark TRECVID data demonstrate that the proposed framework is able to handle large-scale and imbalanced video data with promising classification and retrieval performance.

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Lv, Xiao, Yong Jie Li, and Xu Lu. "A Web Data Mining Algorithm Based on Weighted Association Rules." Key Engineering Materials 467-469 (February 2011): 1386–91. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.1386.

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Association rules mining is attracting much attention in research community due to its broad applications. Existing web data mining methods suffer the problems that 1) the large number of candidate itemsets, which are hard to be pruned, should be pruned in advance. 2) the time of scanning the database, which are needed to scan transactional database repeatedly, should be reduced. In this paper, a new association rules mining model is introduced for overcoming above two problems. We develop an efficient algorithm-WARDM(Weighted Association Rules Data Mining) for mining the candidate itemsets. The algorithm discusses the generation of candidate-1 itemset, candidate-2 itemset and candidate-k itemset(k>2),which can avoid missing weighted frequent itemsets. And the transactional database are scanned only once and candidate itemsets are pruned twice, which can reduce the amount of candidate itemsets. Theoretical analysis and experimental results show the space and time complexity is relatively good, Meanwhile the algorithm decreases the number of candidate itemsets, enhances the execution efficiency.

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Cai, Jiang hui, Xu jun Zhao, and Ya lin Xun. "Association rule mining method based on weighted frequent pattern tree in mobile computing environment." International Journal of Wireless and Mobile Computing 6, no. 2 (2013): 193. http://dx.doi.org/10.1504/ijwmc.2013.054047.

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Faridi, Mainaz, Seema Verma, and Saurabh Mukherjee. "A novel algorithm of weighted fuzzy spatial association rule mining (WFSARM) for wasteland reclamation." Journal of Information and Optimization Sciences 39, no. 1 (November 10, 2017): 195–211. http://dx.doi.org/10.1080/02522667.2017.1372920.

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Wang, Tianxiong, and Meiyu Zhou. "Integrating rough set theory with customer satisfaction to construct a novel approach for mining product design rules." Journal of Intelligent & Fuzzy Systems 41, no. 1 (August 11, 2021): 331–53. http://dx.doi.org/10.3233/jifs-201829.

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When users choose a product, they consider the emotional experience triggered by the product form. In view of the fact that traditional kansei engineering can not effectively reflect the complex and changeable psychological factors of users, and it has not explored the complex relationship between customer satisfaction and perceptual demand characteristics. To address this problem, some uncertainty techniques including rough sets and fuzzy sets are applied to capture more accurate emotion knowledge. Therefore, this research proposes an integrated evaluation gird method (EGM), rough set theory (RST), continuous fuzzy kano model (CFKM), fuzzy weighted association rule mining method to extract the significant relationship between user needs and product morphological features. The EGM is applied to analyze the attractive factor of morphological characteristics of the product, and then the demand items with the highest satisfaction are analyzed through CFKM. The semantic difference method is combined to construct a decision table, and through attribute reduction and importance calculation to obtain the weight of the core product design items. In order to explore the non-linear relationship between design elements and kansei images, the fuzzy weighted association rule mining method was applied to obtain the set of frequent fuzzy weighted association rules based on evidence theory’s reliability indices of minimum support and confidence so as to realize user demand-driven product design. Taking the design of electric bicycle as an example, the experiment results show that the proposed method can help companies or designers develop products to generate good solutions for customer need.

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Kang, Xinhui, Caroline Samantha Porter, and Erik Bohemia. "Using the fuzzy weighted association rule mining approach to develop a customer satisfaction product form." Journal of Intelligent & Fuzzy Systems 38, no. 4 (April 30, 2020): 4343–57. http://dx.doi.org/10.3233/jifs-190957.

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Nithya, NS, and K. Duraiswamy. "Correlated gain ratio based fuzzy weighted association rule mining classifier for diagnosis health care data." Journal of Intelligent & Fuzzy Systems 29, no. 4 (October 23, 2015): 1453–64. http://dx.doi.org/10.3233/ifs-151614.

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Kim, YongSeog. "Streaming association rule (SAR) mining with a weighted order-dependent representation of Web navigation patterns." Expert Systems with Applications 36, no. 4 (May 2009): 7933–46. http://dx.doi.org/10.1016/j.eswa.2008.10.068.

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Qi, Jianfang, Xin Mou, Yue Li, Xiaoquan Chu, and Weisong Mu. "A novel consumer preference mining method based on improved weclat algorithm." Journal of Enterprising Communities: People and Places in the Global Economy 16, no. 1 (October 11, 2021): 74–92. http://dx.doi.org/10.1108/jec-08-2021-0113.

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Purpose Conventional frequent itemsets mining ignores the fact that the relative benefits or significance of “transactions” belonging to different customers are different in most of the relevant applied studies, which leads to failure to obtain some association rules with lower support but from higher-value consumers. Because not all customers are financially attractive to firms, it is necessary that their values be determined and that transactions be weighted. The purpose of this study is to propose a novel consumer preference mining method based on conventional frequent itemsets mining, which can discover more rules from the high-value consumers. Design/methodology/approach In this study, the authors extend the conventional association rule problem by associating the “annual purchase amount” – “price preference” (AP) weight with a consumer to reflect the consumer’s contribution to a market. Furthermore, a novel consumer preference mining method, the AP-weclat algorithm, is proposed by introducing the AP weight into the weclat algorithm for discovering frequent itemsets with higher values. Findings The experimental results from the survey data revealed that compared with the weclat algorithm, the AP-weclat algorithm can make some association rules with low support but a large contribution to a market pass the screening by assigning different weights to consumers in the process of frequent itemsets generation. In addition, some valuable preference combinations can be provided for related practitioners to refer to. Originality/value This study is the first to introduce the AP-weclat algorithm for discovering frequent itemsets from transactions through considering AP weight. Moreover, the AP-weclat algorithm can be considered for application in other markets.

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Kharya, Shweta, Edeh Michael Onyema, Aasim Zafar, Mohd Anas Wajid, Rockson Kwasi Afriyie, Tripti Swarnkar, and Sunita Soni. "Weighted Bayesian Belief Network: A Computational Intelligence Approach for Predictive Modeling in Clinical Datasets." Computational Intelligence and Neuroscience 2022 (July 20, 2022): 1–8. http://dx.doi.org/10.1155/2022/3813705.

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There are growing concerns about the mortality due to Breast cancer many of which often result from delayed detection and treatment. So an effective computational approach is needed to develop a predictive model which will help patients and physicians to manage the situation timely. This study presented a Weighted Bayesian Belief Network (WBBN) modeling for breast cancer prediction using the UCI breast cancer dataset. New automated ranking method was used to assign proper weights to attribute value pair based on their impact on causing the disease. Association between attributes was generated using weighted association rule mining between two attributes, multiattributes, and with class labels to generate rules. Weighted Bayesian confidence and weighted Bayesian lift measures were used to produce strong rules to build the model. To build WBBN, the Open Markov tool was used for structure and parametric learning using generated strong rules. The model was trained using 70% records and tested on 30% records with a threshold value of minimum support = 36% and confidence = 70% which produced results with an accuracy of 97.18%. Experimental results show that WBBN achieved better results in most cases compared to other predictive models. The study would contribute to the fight against breast cancer and the quality of treatment.

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Subbulakshmi, B., and C. Deisy. "An improved incremental algorithm for mining weighted class-association rules." International Journal of Business Intelligence and Data Mining 13, no. 1/2/3 (2018): 291. http://dx.doi.org/10.1504/ijbidm.2018.088437.

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Agapito, Giuseppe, Mario Cannataro, Pietro Hiram Guzzi, and Marianna Milano. "Using GO-WAR for mining cross-ontology weighted association rules." Computer Methods and Programs in Biomedicine 120, no. 2 (July 2015): 113–22. http://dx.doi.org/10.1016/j.cmpb.2015.03.007.

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KIM, Jungja, Heetaek CEONG, and Yonggwan WON. "Weighted Association Rule Mining for Item Groups with Different Properties and Risk Assessment for Networked Systems." IEICE Transactions on Information and Systems E92-D, no. 1 (2009): 10–15. http://dx.doi.org/10.1587/transinf.e92.d.10.

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Jayaprakash, Aiswarya, and Bhavithra Janakiraman. "An Improved Mechanism for User Profile Generation Using Case-Based Reasoning and Weighted Association Rule Mining." International Journal of Computer Sciences and Engineering 6, no. 12 (December 31, 2018): 803–9. http://dx.doi.org/10.26438/ijcse/v6i12.803809.

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