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Journal articles on the topic 'Fuzzy association mining'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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1

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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2

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

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Temporal data mining generate temporal association rule that encapsulate transaction of item with time that’s recorded in temporal data base. Now these days recent research has focused to generate efficient fuzzy temporal association rule and transforming each quantitative value into fuzzy sets using the given membership functions. This paper presents a survey on temporal association rule and fuzzy logic. The Technical constraint of temporal data mining and fuzzy logic are identified and presented.
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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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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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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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8

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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9

Rajkamal Sarma. "Discovery of Fuzzy and Composite Fuzzy Association Rules in Meteorological Data." Journal of Information Systems Engineering and Management 10, no. 37s (2025): 677–97. https://doi.org/10.52783/jisem.v10i37s.6505.

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Fuzzy Association Rule Mining (FARM) extends traditional ARM by evaluating and pruning rules based on interestingness measures to identify relevant patterns for various applications. The focus of this paper is to explore the application of FARM techniques demonstrating its algorithmic implementation in a meteorological dataset. Three major algorithms known as fuzzy Apriori, FTDA (Fuzzy Transaction Data-Mining Algorithm) and CFARM Composite Fuzzy Association Rule Mining) are experimented and analyzed. The experiment uses a real meteorological dataset spanning twenty years consisting some important attributes of weather such as rainfall, temperature, relative humidity, wind speed and bright sunshine hours of the North Bank Plain Zone (NBPZ) of the Brahmaputra River in Assam, India. The collected dataset is pre-processed into a transaction dataset and converted into a fuzzy dataset using membership functions. The three FARM algorithms are subsequently employed to uncover associations among various attributes within the fuzzy meteorological dataset. This study analyzes experimental results from three algorithms, focusing on factors like rule generation, computation time, and memory consumption. While Fuzzy Apriori provides comprehensive rule generation, it comes at the cost of higher computation time and memory usage. FTDA and CFARM, on the other hand, offer more efficient and significant rule generation, making them more suitable for large-scale, complex data analysis. The findings of this paper can contribute to the development of resilient and efficient data mining frameworks, enhancing the decision-making process for stakeholders in the meteorological domain. Thus, the paper introduces a new method for analyzing meteorological data using Fuzzy Association Rule Mining (FARM) techniques.
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Intan, Rolly, Oviliani Yenty Yuliana, and Dwi Kristanto. "Bayesian Belief Network untuk Menghasilkan Fuzzy Association Rules." Jurnal Teknik Industri 12, no. 1 (2010): 55–60. http://dx.doi.org/10.9744/jti.12.1.55-60.

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Bayesian Belief Network (BBN), one of the data mining classification methods, is used in this research for mining and analyzing medical track record from a relational data table. In this paper, a mutual information concept is extended using fuzzy labels for determining the relation between two fuzzy nodes. The highest fuzzy information gain is used for mining fuzzy association rules in order to extend a BBN. Meaningful fuzzy labels can be defined for each domain data. For example, fuzzy labels of secondary disease and complication disease are defined for a disease classification. The implemented of the extended BBN in a application program gives a contribution for analyzing medical track record based on BBN graph and conditional probability tables.
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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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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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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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Guo, Jia Mei, and Yin Xiang Pei. "Association Rules Mining Based on Adaptive Fuzzy Clustering Algorithm." Advanced Materials Research 998-999 (July 2014): 842–45. http://dx.doi.org/10.4028/www.scientific.net/amr.998-999.842.

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Association rules extraction is one of the important goals of data mining and analyzing. Aiming at the problem that information lose caused by crisp partition of numerical attribute , in this article, we put forward a fuzzy association rules mining method based on fuzzy logic. First, we use c-means clustering to generate fuzzy partitions and eliminate redundant data, and then map the original data set into fuzzy interval, in the end, we extract the fuzzy association rules on the fuzzy data set as providing the basis for proper decision-making. Results show that this method can effectively improve the efficiency of data mining and the semantic visualization and credibility of association rules.
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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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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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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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18

Shi, Wenzhong, Anshu Zhang, and Geoffrey I. Webb. "Mining significant crisp-fuzzy spatial association rules." International Journal of Geographical Information Science 32, no. 6 (2018): 1247–70. http://dx.doi.org/10.1080/13658816.2018.1434525.

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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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Hu, Yi-Chung, Ruey-Shun Chen, and Gwo-Hshiung Tzeng. "Mining fuzzy association rules for classification problems." Computers & Industrial Engineering 43, no. 4 (2002): 735–50. http://dx.doi.org/10.1016/s0360-8352(02)00136-5.

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21

Chen, Yen-Liang, and Cheng-Hsiung Weng. "Mining fuzzy association rules from questionnaire data." Knowledge-Based Systems 22, no. 1 (2009): 46–56. http://dx.doi.org/10.1016/j.knosys.2008.06.003.

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22

Weng, Cheng-Hsiung, and Yen-Liang Chen. "Mining fuzzy association rules from uncertain data." Knowledge and Information Systems 23, no. 2 (2009): 129–52. http://dx.doi.org/10.1007/s10115-009-0223-1.

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23

Yan, Chun, Jiahui Liu, Wei Liu, and Xinhong Liu. "Research on automobile insurance fraud identification based on fuzzy association rules." Journal of Intelligent & Fuzzy Systems 41, no. 6 (2021): 5821–34. http://dx.doi.org/10.3233/jifs-201301.

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With the development of automobile insurance industry, how to identify automobile insurance fraud from massive data becomes particularly important. The purpose of this paper is to improve automobile insurance fraud management and explore the application of data mining technology in automobile insurance fraud identification. To this aim, an Apriori algorithm based on simulated annealing genetic fuzzy C-means (SAGFCM-Apriori) have been proposed. The SAGFCM-Apriori algorithm combines fuzzy theory with association rule mining, expanding the application scope of the Apriori algorithm. Considering that the clustering center of the traditional fuzzy C-means (FCM) algorithm is easy to fall into local optimal, the simulated annealing genetic (SAG) algorithm is used to optimize it. The SAG algorithm optimized FCM (SAGFCM) is used to generate fuzzy membership degrees and introduces fuzzy data into the Apriori algorithm. The Apriori algorithm is improved by reducing the rule mining time when acquiring rules. The results of empirical studies on several data sets demonstrate that the optimization of FCM by SAG can effectively avoid the local optimal problem, improve the accuracy of clustering, and enable SAGFCM-Apriori to obtain better fuzzy data during data preprocessing. Moreover, the proposed algorithm can reduce the mining time of association rules and improve mining efficiency. Finally, the SAGFCM-Apriori algorithm is applied to the scene of automobile insurance fraud identification, and the automobile insurance fraud data is mined to obtain fuzzy association rules that can identify fraud claims.
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Lee, Keon-Myung. "Generalized Fuzzy Quantitative Association Rules Mining with Fuzzy Generalization Hierarchies." International Journal of Fuzzy Logic and Intelligent Systems 2, no. 3 (2002): 210–14. http://dx.doi.org/10.5391/ijfis.2002.2.3.210.

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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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Ramdasi, Swati R. "Introducing Concept of Fuzzy Support Matrix for Interestingness Measures." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–15. http://dx.doi.org/10.55041/ijsrem36778.

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Fuzzy association rules with its linguistic annotations and human interpretable form, has provided a convenient extension of association concepts to quantified attributes. The applicability is extended by combining extraction of both positive and negative association rules. Interestingness measures are used to filter out the useful and correct set of actionable association rules from the larger set of rules mined by association rule mining algorithms. Many measures such as Support, Confidence, Conviction and Certainty Factor, with their own area of applicability and statistical significance are popular. The wide range of measures is usually based on frequency counts or probability of occurrence of certain attribute patterns. Binary attributes uses a 2×2 contingency table as the basis for defining different measures. This paper presents concept of fuzzy support matrix using fuzzy partitions, as a natural extension of contingency table for the different interestingness measures. Those can be defined in a uniform and consistent manner. It uses the existing interestingness measures defined in new form using fuzzy support and illustrate these concepts using known data sets. This paper represent active research directions aimed at advancing the capabilities, applicability, and efficiency of fuzzy association rule mining in handling modern data challenges across various domains. Keywords: Interestingness measures; Association Rules mining; Fuzzy sets.
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Wei, Qian. "Product Shape Design Scheme Evaluation Method Based on Spatial Data Mining." Mathematical Problems in Engineering 2022 (July 20, 2022): 1–8. http://dx.doi.org/10.1155/2022/3231357.

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The stage of product modeling design implies a lot of complex tacit knowledge, which is the embodiment of the design concept centered on product modeling design and is also the hot spot and difficulty of modern design theory and method research. Aiming at the evaluation and decision of product modeling design scheme, a decision-making method of approaching ideal solution ranking based on grey relational analysis was proposed, which realized the convergence of tacit knowledge. The empty association rule is an important knowledge content of spatial data mining. A fuzzy genetic algorithm can solve the characteristics of random and nonlinear problems and solve the data mining problems of spatial association rules. The fuzzy genetic algorithm of discrete crossover probability and mutation probability is applied to data mining of spatial association rules in a spatial database, the coding method of the fuzzy genetic algorithm and the construction of fitness function are discussed, and the process of mining spatial association rules is given. The results show that the method of mining s association rules with the fuzzy genetic algorithm is feasible and has higher mining efficiency. This paper discusses the construction method of designing a decision support database based on linear regression and neural network and then proposes a decision method combining TOPSIS and grey relational analysis, which comprehensively considers the position and shape of the scheme data curve.
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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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Petry, Frederick, and Ronald Yager. "Data Mining Using Association Rules for Intuitionistic Fuzzy Data." Information 14, no. 7 (2023): 372. http://dx.doi.org/10.3390/info14070372.

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This paper considers approaches to the computation of association rules for intuitionistic fuzzy data. Association rules can provide guidance for assessing the significant relationships that can be determined while analyzing data. The approach uses the cardinality of intuitionistic fuzzy sets that provide a minimum and maximum range for the support and confidence metrics. A new notation is used to enable the representation of the fuzzy metrics. A running example of queries about the desirable features of vacation locations is used to illustrate.
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Hameed, S., F. Shahzad, and S. Asghar. "SANITIZING SENSITIVE ASSOCIATION RULES USING FUZZY CORRELATION SCHEME." Nucleus 50, no. 4 (2013): 359–67. https://doi.org/10.71330/thenucleus.2013.739.

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Data mining is used to extract useful information hidden in the data. Sometimes this extraction of information leads to revealing sensitive information. Privacy preservation in Data Mining is a process of sanitizing sensitive information. This research focuses on sanitizing sensitive rules discovered in quantitative data. The proposed scheme, Privacy Preserving in Fuzzy Association Rules (PPFAR) is based on fuzzy correlation analysis. In this work, fuzzy set concept is integrated with fuzzy correlation analysis and Apriori algorithm to mark interesting fuzzy association rules. The identified rules are called sensitive. For sanitization, we use modification technique where we substitute maximum value of fuzzy items with zero, which occurs most frequently. Experiments demonstrate that PPFAR method hides sensitive rules with minimum modifications. The technique also maintains the modified data’s quality. The PPFAR scheme has applications in various domains e.g. temperature control, medical analysis, travel time prediction, genetic behavior prediction etc. We have validated the results on medical dataset.
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Wu, Jie. "Evaluation Model of Product Shape Design Scheme Based on Fuzzy Genetic Algorithm Mining Spatial Association Rules." Mathematical Problems in Engineering 2022 (March 18, 2022): 1–10. http://dx.doi.org/10.1155/2022/2888472.

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Put forward a kind of association rules mining method based on fuzzy genetic algorithm, this approach by building a mining model, the association rules and fuzzy genetic algorithm fuses in together, and then given to the fitness function of the mining space, and uses threshold to limit the fuzzy genetic algorithm will cross distribution and compile the fitness function, the improved method excavation stability is strong, the mining accuracy is high. The clustering analysis method of multidimensional fuzzy genetic algorithm mapping association network is studied, and the multidimensional module layout target is analyzed by using fuzzy hierarchical analysis technology and improved genetic algorithm combined with the clustering target of each angle, and the module division of each angle is realized. The main structure of the product is constructed with process model as the integration framework, style as the organization form, and feature list as the expression mechanism. The product characteristics based on fuzzy genetic algorithm are studied, the main structure configuration design process model mapping relation analysis, combined with the main structure of the joint model, together to achieve the fuzzy genetic algorithm (GA) variant design of fine-grained axiomatic mode, based on the associated network building and integration of new product design process, product structure of multidimensional optimization problem is solved.
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Gandikota, Ramu, Soumya M, Jayanthi Appawala, Somasekar J., and K. Baseer K. "Protecting big data mining association rules using fuzzy system." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 6 (2019): 3057–65. https://doi.org/10.12928/TELKOMNIKA.v17i6.10064.

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Recently, big data is granted to be the solution to opening the subsequent large fluctuations of increase in fertility. Along with the growth, it is facing some of the challenges. One of the significant problems is data security. While people use data mining methods to identify valuable information following massive database, people further hold the necessary to maintain any knowledge so while not to be worked out, like delicate common itemsets, practices, taxonomy tree and the like Association rule mining can make a possible warning approaching the secrecy of information. So, association rule hiding methods are applied to evade the hazard of delicate information misuse. Various kinds of investigation already prepared on association rule protecting. However, maximum of them concentrate on introducing methods with a limited view outcome for inactive databases (with only existing information), while presently the researchers facing the problem with continuous information. Moreover, in the era of big data, this is essential to optimize current systems to be suited concerning the big data. This paper proposes the framework is achieving the data anonymization by using fuzzy logic by supporting big data mining. The fuzzy logic grouping the sensitivity of the association rules with a suitable association level. Moreover, parallelization methods which are inserted in the present framework will support fast data mining process.
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Luo, Jianxiong, and Susan M. Bridges. "Mining fuzzy association rules and fuzzy frequency episodes for intrusion detection." International Journal of Intelligent Systems 15, no. 8 (2000): 687–703. http://dx.doi.org/10.1002/1098-111x(200008)15:8<687::aid-int1>3.0.co;2-x.

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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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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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36

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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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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Wang, Jie, Qin Hua Dang, and Zhen Jie Wu. "Mining Model of Fuzzy Association Rules and its Application in Calciner." Advanced Materials Research 219-220 (March 2011): 904–7. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.904.

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The fuzzy clustering method is used to divide the data of calciner in this paper, and the method of fuzzy association rules is applied to get the control rules of calciner. Firstly, the fuzzy cluster and association algorithm are discussed,, later the hybrid algorithm KFCM is utilized to calculate the real data from the calciner to get the membership of each data, and MFAR method is used to obtain effective fuzzy association rules. The way put forward in this paper can solve the bottlenecks of expert experience, and can provide theoretical basis for the control of calciner.
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39

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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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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Jain, V., S. Wadhwa, and S. G. Deshmukh. "Supplier selection using fuzzy association rules mining approach." International Journal of Production Research 45, no. 6 (2007): 1323–53. http://dx.doi.org/10.1080/00207540600665836.

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Hong, Tzung-Pei, Kuei-Ying Lin, and Shyue-Liang Wang. "Fuzzy data mining for interesting generalized association rules." Fuzzy Sets and Systems 138, no. 2 (2003): 255–69. http://dx.doi.org/10.1016/s0165-0114(02)00272-5.

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Palacios, A. M., M. J. Gacto, and J. Alcalá-Fdez. "Mining fuzzy association rules from low-quality data." Soft Computing 16, no. 5 (2011): 883–901. http://dx.doi.org/10.1007/s00500-011-0775-3.

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Lu, Jian-jiang, Bao-wen Xu, Xiao-feng Zou, Da-zhou Kang, Yan-hui Li, and Jin Zhou. "Parallel mining and application of fuzzy association rules." Frontiers of Electrical and Electronic Engineering in China 1, no. 2 (2006): 177–82. http://dx.doi.org/10.1007/s11460-006-0024-1.

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Au, Wai-Ho, and Keith C. C. Chan. "Mining changes in association rules: a fuzzy approach." Fuzzy Sets and Systems 149, no. 1 (2005): 87–104. http://dx.doi.org/10.1016/j.fss.2004.07.018.

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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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Chen, Guoqing, and Qiang Wei. "Fuzzy association rules and the extended mining algorithms." Information Sciences 147, no. 1-4 (2002): 201–28. http://dx.doi.org/10.1016/s0020-0255(02)00264-5.

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Chen, Chun-Hao, Guo-Cheng Lan, Tzung-Pei Hong, and Shih-Bin Lin. "Mining fuzzy temporal association rules by item lifespans." Applied Soft Computing 41 (April 2016): 265–74. http://dx.doi.org/10.1016/j.asoc.2016.01.008.

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