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Journal articles on the topic 'Fuzzy Association Rule Mining'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 February 2022

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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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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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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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Kalia, Harihar, Satchidananda Dehuri, and Ashish Ghosh. "A Survey on Fuzzy Association Rule Mining." International Journal of Data Warehousing and Mining 9, no. 1 (2013): 1–27. http://dx.doi.org/10.4018/jdwm.2013010101.

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Association rule mining is one of the fundamental tasks of data mining. The conventional association rule mining algorithms, using crisp set, are meant for handling Boolean data. However, in real life quantitative data are voluminous and need careful attention for discovering knowledge. Therefore, to extract association rules from quantitative data, the dataset at hand must be partitioned into intervals, and then converted into Boolean type. In the sequel, it may suffer with the problem of sharp boundary. Hence, fuzzy association rules are developed as a sharp knife to solve the aforesaid problem by handling quantitative data using fuzzy set. In this paper, the authors present an updated survey of fuzzy association rule mining procedures along with a discussion and relevant pointers for further research.

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Bai, Yi Ming, Xian Yao Meng, and Xin Jie Han. "Mining Fuzzy Association Rules in Quantitative Databases." Applied Mechanics and Materials 182-183 (June 2012): 2003–7. http://dx.doi.org/10.4028/www.scientific.net/amm.182-183.2003.

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In this paper, we introduce a novel technique for mining fuzzy association rules in quantitative databases. Unlike other data mining techniques who can only discover association rules in discrete values, the algorithm reveals the relationships among different quantitative values by traversing through the partition grids and produces the corresponding Fuzzy Association Rules. Fuzzy Association Rules employs linguistic terms to represent the revealed regularities and exceptions in quantitative databases. After the fuzzy rule base is built, we utilize the definition of Support Degree in data mining to reduce the rule number and save the useful rules. Throughout this paper, we will use a set of real data from a wine database to demonstrate the ideas and test the models.

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Pardeshi, Pramod, and Ujwala Patil. "Fuzzy Association Rule Mining- A Survey." International Journal of Scientific Research in Computer Science and Engineering 5, no. 6 (2017): 13–18. http://dx.doi.org/10.26438/ijsrcse/v5i6.1318.

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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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Abouzakhar, Nasser S., Huankai Chen, and Bruce Christianson. "An Enhanced Fuzzy ARM Approach for Intrusion Detection." International Journal of Digital Crime and Forensics 3, no. 2 (2011): 41–61. http://dx.doi.org/10.4018/jdcf.2011040104.

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The integration of fuzzy logic with data mining methods such as association rules has achieved interesting results in various digital forensics applications. As a data mining technique, the association rule mining (ARM) algorithm uses ranges to convert any quantitative features into categorical ones. Such features lead to the sudden boundary problem, which can be smoothed by incorporating fuzzy logic so as to develop interesting patterns for intrusion detection. This paper introduces a Fuzzy ARM-based intrusion detection model that is tested on the CAIDA 2007 backscatter network traffic dataset. Moreover, the authors present an improved algorithm named Matrix Fuzzy ARM algorithm for mining fuzzy association rules. The experiments and results that are presented in this paper demonstrate the effectiveness of integrating fuzzy logic with association rule mining in intrusion detection. The performance of the developed detection model is improved by using this integrated approach and improved algorithm.

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A, Anitha, and Freeda Jebamalar.S. "Predicting Dengue Using Fuzzy Association Rule Mining." International Journal of Computer Trends and Technology 67, no. 3 (2019): 72–74. http://dx.doi.org/10.14445/22312803/ijctt-v67i3p114.

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Watanabe, Toshihiko. "An Improvement of Fuzzy Association Rules Mining Algorithm Based on Redundancy of Rules." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 9 (2011): 1248–55. http://dx.doi.org/10.20965/jaciii.2011.p1248.

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In data mining approach, quantitative attributes should be appropriately dealt with as well as Boolean attributes. This paper presents an essential improvement for extracting fuzzy association rules from a database. The objective of this paper is to improve the computational time of mining and to prune extracted redundant rules simultaneously for an actual data mining application. In this paper, we define the redundancy of fuzzy association rules as a new concept for mining and prove essential theorems concerning the redundancy of fuzzy association rules. Then, we propose a basic algorithm based on the Apriori algorithm for rule extraction utilizing the redundancy of the extracted rules. The essential performance of the algorithmis evaluated through numerical experiments using benchmark data. Fromthe results, themethod is found to be promising in terms of computational time and redundant-rule pruning.

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Lee, Carmen Kar Hang, Y. K. Tse, G. T. S. Ho, and K. L. Choy. "Fuzzy association rule mining for fashion product development." Industrial Management & Data Systems 115, no. 2 (2015): 383–99. http://dx.doi.org/10.1108/imds-09-2014-0277.

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Purpose – The emergence of the fast fashion trend has exerted a great pressure on fashion designers who are urged to consider customers’ preferences in their designs and develop new products in an efficient manner. The purpose of this paper is to develop a fuzzy association rule mining (FARM) approach for improving the efficiency and effectiveness of new product development (NPD) in fast fashion. Design/methodology/approach – The FARM identifies the hidden relationships between product styles and customer preferences. The knowledge discovered help the fashion industry design new products which are not only fashionable, but are also saleable in the market. Findings – To evaluate the proposed approach, a case study is conducted in a Hong Kong-based fashion company in which a real-set of data are tested to generate fuzzy association rules. The results reveal that the FARM approach can provide knowledge support to the fashion industry during NPD, shorten the NPD cycle time, and increase customer satisfaction. Originality/value – Compared with traditional association rule mining, the proposed FARM approach takes the fuzziness of data into consideration and the knowledge represented in the fuzzy rules is in a more human-understandable structure. It captures the voice of the customer into fashion product development and provides a specific solution to deal with the challenges brought by fast fashion. In addition, it helps increase the innovation and technological capability of the fashion industry.

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Li, Tianyu, Fangyan Dong, and Kaoru Hirota. "Fuzzy Association Rule Mining Based Myocardial Ischemia Diagnosis on ECG Signal." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 2 (2015): 217–24. http://dx.doi.org/10.20965/jaciii.2015.p0217.

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A fuzzy association rule mining based method is proposed for myocardial ischemia diagnosis on ECG signals. The proposal provides interpretable and understandable information to doctors as an assistant reference, while rule mining on fuzzy itemsets guarantees that the feature segmentation before rule extraction is feasible and effective. A set of fuzzy association rules is mined through experiments on data from the European ST-T Database, and classification results of myocardial ischemia and normal heartbeats on the test dataset using the extracted rules obtained values of 83.4%, 80.7%, and 81.4% for sensitivity, specificity, and accuracy, respectively. The proposed method aims to become a helpful tool to accelerate the diagnosis of myocardial ischemia on ECG signal, and to be expanded to other heart disease diagnosis areas such as hypertensive heart disease and arrhythmia.

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Jiebing Liu, Baoxiang Liu, Jianming Liu, and Huanhuan Chen. "Association Rule Mining Algorithm Based On Fuzzy Association Rules Lattice and Apriori." Journal of Convergence Information Technology 8, no. 8 (2013): 399–406. http://dx.doi.org/10.4156/jcit.vol8.issue8.48.

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CHEN, CHUN-HAO, TZUNG-PEI HONG, and YEONG-CHYI LEE. "GENETIC-FUZZY MINING WITH TAXONOMY." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 20, supp02 (2012): 187–205. http://dx.doi.org/10.1142/s021848851240020x.

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Data mining is most commonly used in attempts to induce association rules from transaction data. Since transactions in real-world applications usually consist of quantitative values, many fuzzy association-rule mining approaches have been proposed on single- or multiple-concept levels. However, the given membership functions may have a critical influence on the final mining results. In this paper, we propose a multiple-level genetic-fuzzy mining algorithm for mining membership functions and fuzzy association rules using multiple-concept levels. It first encodes the membership functions of each item class (category) into a chromosome according to the given taxonomy. The fitness value of each individual is then evaluated by the summation of large 1-itemsets of each item in different concept levels and the suitability of membership functions in the chromosome. After the GA process terminates, a better set of multiple-level fuzzy association rules can then be expected with a more suitable set of membership functions. Experimental results on a simulation dataset also show the effectiveness of the algorithm.

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Rindengan, Altin J. "PERBANDINGAN ASOSSIATION RULE BERBENTUK BINER DAN FUZZY C-PARTITION PADA ANALISIS MARKET BASKET DALAM DATA MINING." JURNAL ILMIAH SAINS 12, no. 2 (2012): 135. http://dx.doi.org/10.35799/jis.12.2.2012.717.

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PERBANDINGAN ASOSSIATION RULE BERBENTUK BINER DAN FUZZY C-PARTITION PADA ANALISIS MARKET BASKET DALAM DATA MININGABSTRAKSalah satu analisis dalam data mining adalah market basket analysis untuk menganalisa kecenderungan pembelian suatu barang yang berasosiasi dengan barang yang lain. Dalam tulisan ini membahas aturan asosiasinya dengan mempertimbangkan jumlah item barang yang dibeli dalam satu transaksi. Asumsinya adalah keterkaitan pembelian suatu barang dengan barang yang lain dalam satu transaksi akan semakin kecil jika jumlah item barang yang dibeli semakin banyak. Tulisan ini menganalisa asosisasi antar item barang dengan membuat tabel transaksi dalam bentuk nilai fuzzy set dibandingkan dengan analisa asosiasi yang biasa dilakukan dalam bentuk biner. Berdasarkan analisis terhadap data yang digunakan memberikan hasil support dan confidence yang cenderung lebih kecil tetapi lebih realistis dibanding aturan asosisasi biasa. Keywords: analisis market basket, association rule, data mining, fuzzy c-partition.COMPARISON OF ASSOCIATION RULE WITH BINARY AND FUZZY C-PARTITION FORM AT MARKET BASKET ANALYSIS ON DATA MININGABSTRACTOne analysis in data mining is market basket analysis to analyze the purchase of a good trends associated with other items. In this paper discussing the association rules by considering the number of items purchased in one transaction. The assumption is that the purchase of a good relationship with the other items in one transaction will be smaller if the number of items purchased items more and more. This paper analyzes the association between the items of goods by making the transaction table in the form of fuzzy sets of values to compare with analysis of the usual associations in binary form. Based on the analysis of the data used to support and confidence of which tend to be smaller but more realistic than usual asosisasi rules. Keywords: market basket analysis, association rule, data mining, fuzzy c-partition.

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Srivastava, Deepesh Kumar, Basav Roychoudhury, and Harsh Vardhan Samalia. "Fuzzy association rule mining for economic development indicators." International Journal of Intelligent Enterprise 6, no. 1 (2019): 3. http://dx.doi.org/10.1504/ijie.2019.100030.

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Srivastava, Deepesh Kumar, Harsh Vardhan Samalia, and Basav Roychoudhury. "Fuzzy association rule mining for economic development indicators." International Journal of Intelligent Enterprise 6, no. 1 (2019): 3. http://dx.doi.org/10.1504/ijie.2019.10021610.

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Roy, Aritra. "A Survey on Fuzzy Association Rule Mining Methodologies." IOSR Journal of Computer Engineering 15, no. 6 (2013): 01–08. http://dx.doi.org/10.9790/0661-1560108.

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Chaturvedi, Kapil, Dr Ravindra Patel, and Dr D. K. Swami. "A Fuzzy Inference Approach for Association Rule Mining." IOSR Journal of Computer Engineering 16, no. 6 (2014): 57–66. http://dx.doi.org/10.9790/0661-16615766.

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Mahmoodian, Hamid, M. Hamiruce Marhaban, Raha Abdulrahim, Rozita Rosli, and Iqbal Saripan. "Using fuzzy association rule mining in cancer classification." Australasian Physical & Engineering Sciences in Medicine 34, no. 1 (2011): 41–54. http://dx.doi.org/10.1007/s13246-011-0054-8.

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Zheng, Hui, Jing He, Guangyan Huang, Yanchun Zhang, and Hua Wang. "Dynamic optimisation based fuzzy association rule mining method." International Journal of Machine Learning and Cybernetics 10, no. 8 (2018): 2187–98. http://dx.doi.org/10.1007/s13042-018-0806-9.

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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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Li, Zhi Gang, and Feng Li Yang. "The Generation of the Fuzzy Control Rules Based on Association Rules with Temporal Constraints." Applied Mechanics and Materials 385-386 (August 2013): 931–34. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.931.

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In the field of fuzzy control, the generation of fuzzy control rules has always been a problem, because the industrial data is generally expressed in the order of time ,so it strongly depends on the time, it does not take the factors of temporal constraints into account in the previous extracting rule process.This paper uses temporal constraint association rule ,and uses the data mining methods to generate temporal fuzzy control rules. The method is verified by using the MATLAB7.1 ,the simulation shows that the method can achieve good fuzzy control rules.

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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 (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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Matthews, Stephen G., Mario A. Gongora, and Adrian A. Hopgood. "Evolutionary algorithms and fuzzy sets for discovering temporal rules." International Journal of Applied Mathematics and Computer Science 23, no. 4 (2013): 855–68. http://dx.doi.org/10.2478/amcs-2013-0064.

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Abstract A novel method is presented for mining fuzzy association rules that have a temporal pattern. Our proposed method contributes towards discovering temporal patterns that could otherwise be lost from defining the membership functions before the mining process. The novelty of this research lies in exploring the composition of fuzzy and temporal association rules, and using a multi-objective evolutionary algorithm combined with iterative rule learning to mine many rules. Temporal patterns are augmented into a dataset to analyse the method’s ability in a controlled experiment. It is shown that the method is capable of discovering temporal patterns, and the effect of Boolean itemset support on the efficacy of discovering temporal fuzzy association rules is presented.

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Wang, Ling, Qian Ma, and Jianyao Meng. "Incremental Fuzzy Association Rule Mining for Classification and Regression." IEEE Access 7 (2019): 121095–110. http://dx.doi.org/10.1109/access.2019.2933361.

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Sowan, Bilal, Keshav Dahal, M. A. Hossain, Li Zhang, and Linda Spencer. "Fuzzy association rule mining approaches for enhancing prediction performance." Expert Systems with Applications 40, no. 17 (2013): 6928–37. http://dx.doi.org/10.1016/j.eswa.2013.06.025.

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Lu, Nannan, Shingo Mabu, and Kotaro Hirasawa. "Integrated Rule Mining Based on Fuzzy GNP and Probabilistic Classification for Intrusion Detection." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 5 (2011): 495–505. http://dx.doi.org/10.20965/jaciii.2011.p0495.

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With the increasing popularity of the Internet, network security has become a serious problem recently. How to detect intrusions effectively becomes an important component in network security. Therefore, a variety of algorithms have been devoted to this challenge. Genetic network programming is a newly developed evolutionary algorithm with directed graph gene structures, and it has been applied to data mining for intrusion detection systems providing good performances in intrusion detection. In this paper, an integrated rule mining algorithm based on fuzzy GNP and probabilistic classification is proposed. The integrated rule mining uses fuzzy class association rule mining algorithm to extract rules with different classes. Actually, it can deal with both discrete and continuous attributes in network connection data. Then, the classification is done probabilistically using different class rules. The integrated method showed excellent results by simulation experiments.

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Oladipupo, Olufunke O., Charles O. Uwadia, and Charles K. Ayo. "Improving medical rule-based expert systems comprehensibility: fuzzy association rule mining approach." International Journal of Artificial Intelligence and Soft Computing 3, no. 1 (2012): 29. http://dx.doi.org/10.1504/ijaisc.2012.048179.

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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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Verma, Sushil Kumar, R. S. Thakur, and Shailesh Jaloree. "Fuzzy Association Rule Mining based Model to Predict Students’ Performance." International Journal of Electrical and Computer Engineering (IJECE) 7, no. 4 (2017): 2223. http://dx.doi.org/10.11591/ijece.v7i4.pp2223-2231.

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<p class="Abstract">The major intention of higher education institutions is to supply quality education to its students. One approach to get maximum level of quality in higher education system is by discovering knowledge for prediction regarding the internal assessment and end semester examination. The projected work intends to approach this objective by taking the advantage of fuzzy inference technique to classify student scores data according to the level of their performance. In this paper, student’s performance is evaluated using fuzzy association rule mining that describes Prediction of performance of the students at the end of the semester, on the basis of previous database like Attendance, Midsem Marks, Previous semester marks and Previous Academic Records were collected from the student’s previous database, to identify those students which needed individual attention to decrease fail ration and taking suitable action for the next semester examination.</p>

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Duan, Qing, Jian Li, and Yu Wang. "The Application of Fuzzy Association Rule Mining in E-Commerce Information System Mining." Advanced Engineering Forum 6-7 (September 2012): 631–35. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.631.

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Data mining in e-commerce application is information into business knowledge in the process. First of all, the object of clear data mining to determine the theme of business applications; around the commercial main data collection source, and clean up the data conversion, integration processing technology, and selects the appropriate data mining algorithms to build data mining models. This paper presents the application of fuzzy association rule mining in E-commerce information system mining. Experimental data sets prove that the proposed algorithm is effective and reasonable.

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Wirawan, I. Putu Gde Wirarama Wedhaswara, Andy Hidayat Jatmika, and Ariyan Zubaidi. "Sistem Monitoring Tanaman Cerdas Menggunakan Wireless Sensor Network dan Evolutionary Fuzzy Association Rule Mining." Jurnal Teknologi Informasi, Komputer, dan Aplikasinya (JTIKA ) 2, no. 1 (2020): 113–20. http://dx.doi.org/10.29303/jtika.v2i1.94.

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Plant conditions monitoring requires specific knowledge in agriculture. The knowledge includes decision support to describe between good (ideal) and bad conditions from each different plant. Fuzzy rules capable to describes plants bio signal in form of fuzzy membership function that usable for decision support. To simplify the decision support process , this research proposes the design and development of the smart monitoring system of plant conditions based on wireless sensor networks (WSN) and evolutionary fuzzy association rule mining (EFARM). Plant condition monitoring is carried out through sensors input by WSN and decision support algorithm is carried out by EFARM. The proposed method aims to be carried out on a sensor network with supervised learning from training data. The dataset will only be used to create default fuzzy membership functions and rules. Detailed optimization and classification of conditions will be carried out using the evolutionary process by tree based rule extractor from Genetic Programming (GP). The Evaluation has been carried out using three raspberry pi used as EFARM processor and storage, which separated into one central processor and two partial processor for two plants, Cactus and Orchid. The simulation results show that the proposed method is able to extract rules from both plants and is able to measure significant differences between plants.

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D, Siji P., and M. L. Valarmathi . "Data Mining Approach for Feature Reduction Using Fuzzy Association Rule." International Journal of Computer Sciences and Engineering 5, no. 11 (2017): 44–49. http://dx.doi.org/10.26438/ijcse/v5i11.4449.

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Kumar, M. Vijaya, and S. Prakash. "An Improved Sensitive Association Rule Mining using Fuzzy Partition Algorithm." Asian Journal of Research in Social Sciences and Humanities 6, no. 6 (2016): 969. http://dx.doi.org/10.5958/2249-7315.2016.00258.6.

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A.H.M, Sajedul Hoque, Rashed Mustafa, Sujit Kumar Mondal, and Md Al-Amin Bhuiyan. "A Fuzzy Frequent Pattern-Growth Algorithm for Association Rule Mining." International Journal of Data Mining & Knowledge Management Process 5, no. 5 (2015): 21–33. http://dx.doi.org/10.5121/ijdkp.2015.5502.

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Ghosh, Sumana, Navjot Kaur Walia, Parul Kalra, and Deepti Mehrotra. "A fuzzy association rule mining approach using movie lens dataset." CSI Transactions on ICT 4, no. 2-4 (2016): 249–54. http://dx.doi.org/10.1007/s40012-016-0119-7.

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Kaijian, Liang, Liang Quan, and Yang Bingru. "Causal association rule mining methods based on fuzzy state description." Journal of Systems Engineering and Electronics 17, no. 1 (2006): 193–99. http://dx.doi.org/10.1016/s1004-4132(06)60034-0.

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Zhang, Lingling, Yong Shi, and Xinhua Yang. "Association-rule knowledge discovery by using a fuzzy mining approach." International Journal of Business Intelligence and Data Mining 1, no. 4 (2006): 417. http://dx.doi.org/10.1504/ijbidm.2006.010783.

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Krishnamoorthy, Sathiyapriya, G. Sudha Sadasivam, M. Rajalakshmi, K. Kowsalyaa, and M. Dhivya. "Privacy Preserving Fuzzy Association Rule Mining in Data Clusters Using Particle Swarm Optimization." International Journal of Intelligent Information Technologies 13, no. 2 (2017): 1–20. http://dx.doi.org/10.4018/ijiit.2017040101.

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An association rule is classified as sensitive if its thread of revelation is above certain confidence value. If these sensitive rules were revealed to the public, it is possible to deduce sensitive knowledge from the published data and offers benefit for the business competitors. Earlier studies in privacy preserving association rule mining focus on binary data and has more side effects. But in practical applications the transactions contain the purchased quantities of the items. Hence preserving privacy of quantitative data is essential. The main goal of the proposed system is to hide a group of interesting patterns which contains sensitive knowledge such that modifications have minimum side effects like lost rules, ghost rules, and number of modifications. The proposed system applies Particle Swarm Optimization to a few clusters of particles thus reducing the number of modification. Experimental results demonstrate that the proposed approach is efficient in terms of lost rules, number of modifications, hiding failure with complete avoidance of ghost rules.

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Petry, Frederick E. "Data Mining Approaches for Geo-Spatial Big Data." International Journal of Organizational and Collective Intelligence 3, no. 1 (2012): 52–71. http://dx.doi.org/10.4018/joci.2012010104.

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The availability of a vast amount of heterogeneous information from a variety of sources ranging from satellite imagery to the Internet has been termed as the problem of Big Data. Currently there is a great emphasis on the huge amount of geophysical data that has a spatial basis or spatial aspects. To effectively utilize such volumes of data, data mining techniques are needed to manage discovery from such volumes of data. An important consideration for this sort of data mining is to extend techniques to manage the inherent uncertainty involved in such spatial data. In this paper the authors first provide overviews of uncertainty representations based on fuzzy, intuitionistic, and rough sets theory and data mining techniques. To illustrate the issues they focus on the application of the discovery of association rules in approaches for vague spatial data. The extensions of association rule extraction for uncertain data as represented by rough and fuzzy sets are described. Finally an example of rule extraction for both fuzzy and rough set types of uncertainty representations is given

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Li, Cailing, and Wenjun Li. "Automatic Classification Algorithm for Multisearch Data Association Rules in Wireless Networks." Wireless Communications and Mobile Computing 2021 (March 17, 2021): 1–9. http://dx.doi.org/10.1155/2021/5591387.

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In order to realize efficient data processing in wireless network, this paper designs an automatic classification algorithm of multisearch data association rules in a wireless network. According to the algorithm, starting from the mining of multisearch data association rules, from the discretization of continuous attributes of multisearch data, generation of fuzzy classification rules, and the design of association rule classifier and other aspects, automatic classification is completed by using the mining results. Experimental results show that this algorithm has the advantages of small classification error, good real-time performance, high coverage rate, and high feasibility.

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Dash, Satya Ranjan, Satchidananda Dehuri, and Uma kant Sahoo. "Interactions and Applications of Fuzzy, Rough, and Soft Set in Data Mining." International Journal of Fuzzy System Applications 3, no. 3 (2013): 37–50. http://dx.doi.org/10.4018/ijfsa.2013070102.

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In this paper, interactions among fuzzy, rough, and soft set theory has been studied. The authors have examined these theories as a problem solving tool in association rule mining problems of data mining and knowledge discovery in databases. Although fuzzy and rough set have been well studied areas and successfully applied in association rule mining problem, but soft set theory needs more attention from both theoretical and practical side. Therefore, to make some improvement in this direction, the authors studied soft set theory and its interaction with fuzzy and rough set. Alongside, the authors have taken a numerical example related to a societal problem for realizing the practical importance of these theories.

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Kianmehr, Keivan, Mehmet Kaya, Abdallah M. ElSheikh, Jamal Jida, and Reda Alhajj. "Fuzzy association rule mining framework and its application to effective fuzzy associative classification." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1, no. 6 (2011): 477–95. http://dx.doi.org/10.1002/widm.40.

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Kim, Jieun, Mintak Han, Youngjo Lee, and Yongtae Park. "Futuristic data-driven scenario building: Incorporating text mining and fuzzy association rule mining into fuzzy cognitive map." Expert Systems with Applications 57 (September 2016): 311–23. http://dx.doi.org/10.1016/j.eswa.2016.03.043.

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Gao, Jun. "Creation of Fuzzy Control Table with Data Mining." Advanced Materials Research 760-762 (September 2013): 1080–83. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1080.

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A good fuzzy control table is the key to a fuzzy control system, and the systems performance mainly depends on the quality of the table. Based on analyzing fully the principles of a typical fuzzy control systems and the procedures of building a fuzzy control table, this paper presents a new method of applying the boolean association rule data mining techniques to mining of fuzzy control table directly from the database of manual operating records.

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Marín, N., M. D. Ruiz, and D. Sánchez. "Fuzzy frameworks for mining data associations: fuzzy association rules and beyond." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 6, no. 2 (2016): 50–69. http://dx.doi.org/10.1002/widm.1176.

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Ho, G. T. S., W. H. Ip, C. H. Wu, and Y. K. Tse. "Using a fuzzy association rule mining approach to identify the financial data association." Expert Systems with Applications 39, no. 10 (2012): 9054–63. http://dx.doi.org/10.1016/j.eswa.2012.02.047.

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Kuok, Chan Man, Ada Fu, and Man Hon Wong. "Mining fuzzy association rules in databases." ACM SIGMOD Record 27, no. 1 (1998): 41–46. http://dx.doi.org/10.1145/273244.273257.

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