Journal articles: 'Association rule mining. Fuzzy sets'

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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 (December 1, 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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Khan, M. Sulaiman, Maybin Muyeba, Frans Coenen, David Reid, and Hissam Tawfik. "Finding Associations in Composite Data Sets." International Journal of Data Warehousing and Mining 7, no. 3 (July 2011): 1–29. http://dx.doi.org/10.4018/jdwm.2011070101.

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In this paper, a composite fuzzy association rule mining mechanism (CFARM), directed at identifying patterns in datasets comprised of composite attributes, is described. Composite attributes are defined as attributes that can take simultaneously two or more values that subscribe to a common schema. The objective is to generate fuzzy association rules using “properties” associated with these composite attributes. The exemplar application is the analysis of the nutrients contained in items found in grocery data sets. The paper commences with a review of the back ground and related work, and a formal definition of the CFARM concepts. The CFARM algorithm is then fully described and evaluated using both real and synthetic data sets.

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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 (November 10, 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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Petry, Frederick E. "Data Mining Approaches for Geo-Spatial Big Data." International Journal of Organizational and Collective Intelligence 3, no. 1 (January 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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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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Qi, Wei Qiang, Yan Ran Li, Hai Feng Ye, Da Peng Duan, and Xiu Chen Jiang. "Research on Classification of Partial Discharge of Switchgear Cabinets Based on a Novel Association Rule Algorithm." Applied Mechanics and Materials 448-453 (October 2013): 3485–93. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.3485.

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In order to assess switchgear insulation status, a novel association rule mining (ARM) algorithm is presented. It is used to recognize the severity of switchgear cabinet partial discharge. The algorithm uses fuzzy C-means clustering (FCM) to divide partial discharge feature interval, candidate sets meeting minimum support and minimum confidence are sought based on an improved Apriori algorithm. Multiple recursions and scans are performed on candidate sets to generate association rules library for classification. Fuzzy reasoning based on association rules are performed over multiple needle corona partial discharge signals sampled in 10KV switchgear cabinets. The results show that partial discharge classification rate using association rules is high and classification conclusions are accurate. It has provided theoretical basis and practical value for insulation status assessment of switchgear cabinets.

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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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PAPADIMITRIOU, STERGIOS, SEFERINA MAVROUDI, and SPIRIDON D. LIKOTHANASSIS. "MUTUAL INFORMATION CLUSTERING FOR EFFICIENT MINING OF FUZZY ASSOCIATION RULES WITH APPLICATION TO GENE EXPRESSION DATA ANALYSIS." International Journal on Artificial Intelligence Tools 15, no. 02 (April 2006): 227–50. http://dx.doi.org/10.1142/s0218213006002643.

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Fuzzy association rules can reveal useful dependencies and interactions hidden in large gene expression data sets. However their derivation perplexes very difficult combinatorial problems that depend heavily on the size of these sets. The paper follows a divide and conquer approach to the problem that obtains computationally manageable solutions. Initially we cluster genes that more probably are associated. Thereafter, the fuzzy association rule extraction algorithms confront many but significantly reduced computationally problems that usually can be processed very fast. The clustering phase is accomplished by means of an approach based on mutual information (MI). This approach uses the mutual information as a similarity measure. However, the numerical evaluation of the MI is subtle. We experiment with the main methods and we compare between them. As the device that implements the mutual information clustering we use a SOM (Self-Organized Map) based approach that is capable of effectively incorporating supervised bias. After the mutual information clustering phase the fuzzy association rules are extracted locally on a per cluster basis. The paper presents an application of the techniques for mining the gene expression data. However, the presented techniques can easily be adapted and can be fruitful for intelligent exploration of any other similar data set as well.

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Cai, Wentian, and Huijun Yao. "Research on Information Security Risk Assessment Method Based on Fuzzy Rule Set." Wireless Communications and Mobile Computing 2021 (September 22, 2021): 1–12. http://dx.doi.org/10.1155/2021/9663520.

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With the increasing complexity of the network structure and the increasing size of the network, various network security incidents pose an increasing threat to the security of computer systems and the network. Especially, in the network environment, the diversified intrusion methods and application environment make the security of the network more fragile. In order to improve information security, based on fuzzy rule sets, this paper proposes a fuzzy association rule mining algorithm based on fuzzy matrix and applies it to security event correlation. In addition, this paper combines the embedded system to construct an information security risk assessment system and sets the system performance based on the actual situation. Finally, this paper carries out experimental design to verify the performance of the system and analyzes the experimental results by mathematical statistics. From the experimental research, it can be seen that the system constructed in this paper has a certain effect.

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Supriyati, Endang, Mohammad Iqbal, and Tutik Khotimah. "USING SIMILARITY DEGREES TO IMPROVE FUZZY MINING ASSOCIATION RULE BASED MODEL FOR ANALYZING IT ENTREPRENEURIAL TENDENCY." IIUM Engineering Journal 20, no. 2 (December 2, 2019): 78–89. http://dx.doi.org/10.31436/iiumej.v20i2.1096.

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Higher education has great potential in producing new startups in the IT (Information Technology) field. Many choices influence students to become IT- entrepreneurs. Association Rule can be used to obtain a model by analysing data so that it can be used to make a rule to the IT entrepreneurship-student model, but the association algorithm has disadvantages in handling large datasets. We propose reducing candidate itemsets using degrees of fuzzy similarity. The membership function in fuzzy sets can be used to measure the quality of rules obtained. The purpose of this study is to improve the algorithm by evaluating the similarity of candidate itemsets to get a good quality rule. This research method has 2 phases, namely (1) calculating the membership function with similarity itemset and (2) applying fuzzy mining association rule. Phase 1 has several steps, including: preparation of a transaction database, the taxonomy process, and identification of similar itemset. Phase 2 has several steps as well. The first is defining membership functions, and the last is a fuzzy mining fuzzy association rule. In this study, a questionnaire was distributed to 1225 students who were members of the IT entrepreneurship program. The results of this study were reduced into 823 itemsets and produced an IT entrepreneurship rule model. ABSTRAK: Pendidikan tinggi mempunyai potensi besar dalam menghasilkan permulaan baru dalam bidang IT. Banyak pilihan mempengaruhi pelajar bagi menjadi usahawan-IT. Kaedah Bersekutu boleh digunakan bagi mendapatkan model dengan menganalisa data supaya ianya dapat digunakan menjadi model kepada pelajar keusahawanan-IT, namun algoritma bersekutu mempunyai kelemahan dalam mengendalikan dataset yang besar. Kami mencadangkan pengurangan bilangan set item menggunakan tahapan persamaan kabur. Fungsi ahli dalam set kabur dapat digunakan bagi mengukur kualiti aturan yang diperoleh. Tujuan kajian ini adalah bagi meningkatkan algoritma dengan menilai persamaan set item calon bagi mendapatkan aturan kualiti yang baik. Kaedah penyelidikan ini mempunyai 2 peringkat, iaitu (1) mengira fungsi ahli dengan set item persamaan dan (2) menerapkan aturan perlombongan bersekutu kabur. Peringkat 1 mempunyai beberapa langkah, iaitu: urus niaga pangkalan data, proses taksonomi, identifikasi set item yang sama. Tahap 2 mempunyai beberapa langkah, iaitu: menentukan fungsi keahlian, dan akhirnya, aturan perlombongan bersekutu. Dalam kajian ini, soal selidik telah diedarkan kepada 1225 pelajar yang menjadi ahli program keusahawanan IT. Dapatan kajian menunjukkan pengurangan nombor dataset kepada 823 set item dan menghasilkan model aturan teknologi keusahawanan IT.

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Wong, Bennie, G. T. S. Ho, and Eric Tsui. "Development of an intelligent e-healthcare system for the domestic care industry." Industrial Management & Data Systems 117, no. 7 (August 14, 2017): 1426–45. http://dx.doi.org/10.1108/imds-08-2016-0342.

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Purpose In view of the elderly caregiving service being in high demand nowadays, the purpose of this paper is to develop an intelligent e-healthcare system for the domestic care industry by using the Internet of Things (IoTs) and Fuzzy Association Rule Mining (FARM) approach. Design/methodology/approach The IoTs connected with the e-healthcare system collect real-time vital sign monitoring data for the e-healthcare system. The FARM approach helps to identify the hidden relationships between the data records in the e-healthcare system to support the elderly care management tasks. Findings To evaluate the proposed system and approach, a case study was carried out to identify the association between the specific collected demographic data, behavior data and the health measurements data in the e-healthcare system. It is found that the discovered rules are useful for the care management tasks in the elderly healthcare service. Originality/value Knowledge discovery in databases uses various data mining techniques and rule-based artificial intelligence algorithms. This paper demonstrates complete processes on how an e-healthcare system connected with IoTs can support the elderly care services via a data collection phase, data analysis phase and data reporting phase by using the FARM to evaluate the fuzzy sets of the data attributes. The caregivers can use the discovered rules for proactive decision support of healthcare services and to improve the overall service quality by enhancing the elderly healthcare service responsiveness.

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Sadat, Yousef Kanani, Tina Nikaein, and Farid Karimipour. "FUZZY SPATIAL ASSOCIATION RULE MINING TO ANALYZE THE EFFECT OF ENVIRONMENTAL VARIABLES ON THE RISK OF ALLERGIC ASTHMA PREVALENCE." Geodesy and Cartography 41, no. 2 (October 25, 2015): 101–12. http://dx.doi.org/10.3846/20296991.2015.1051339.

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The prevalence of allergic diseases has greatly increased in recent decades, likely due to contamination of the environment with allergy irritants. One common treatment is identifying that allergy irritant, and then avoiding exposure to it. This article studies the relation between the prevalence of allergic asthma and certain allergy irritants that are related to environmental variables. To that end, we use spatial association rule mining to determine the association between the spatial distribution of allergic asthma prevalence and air pollutants such as CO, SO2, NO2, PM10, PM2.5, and O3 (from data compiled by air pollution monitoring stations), as well as other factors, such as the distance of residence from parks and roads. In order to clear up the uncertainties inherent in the attributes linked to the spatial data, the dimensions in question have been defined as fuzzy sets. Results for the case study (i.e. Tehran metropolitan area) indicate that distance to parks and roads, as well as CO, NO2, PM10, and PM2.5 levels are related to allergic asthma prevalence, while SO2 and O3 are not. Finally, we use the extracted association rules in fuzzy inference system to produce the spatial risk map of allergic asthma prevalence, which shows how much is the risk of allergic asthma prevalence at each point of the city.

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Maryum, Ilsa, Waqas Nawaz, and Amad Ud Din. "Hospital management society: A framework based on fuzzy logic and association rule mining towards well-being society." Journal of Intelligent & Fuzzy Systems 39, no. 5 (November 19, 2020): 7123–34. http://dx.doi.org/10.3233/jifs-200349.

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Non-uniformity in medical procedures, expensive medical treatments, and the shortage of medicines in different areas are health care problems in our country. This paper aims to resolve that problem by developing a web-based-application called Hospital Management Society (HMS) based on a novel Dynamic Optimized Fuzzy C-mean Clustering and Association Rule Mining (DOFCCARM). The purpose of HMS is to enhance the hospitals (and clinics) by regulating, overseeing and accrediting them to bring uniformity in health care facilities, to make the medical treatment cost effective, to find common diseases in a particular age and area, and to help government in identifying the areas facing the shortage of licensed medicines. Therefore, HMS creates a single platform for both the doctors of central hospital (CH) and the doctors of member hospitals (MH). The CH provides clinical practice guidelines for various diseases. A team of doctors at CH evaluate the medical treatment provided by MH. If a hospital fails to maintain the standard then HMS blacklists such hospital. In our approach, we take a range of values to distinct successive partitions and generate a parallel membership function to make fuzzy sets of patients report, rather than single partitioning point. We determine the effectiveness of our approach through experiments on a dataset. The results revealed the most common age, symptoms and location for a particular disease and shortage of particular medicine in a specific area.

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Liu, Yan, Ting-Hua Yi, and Zhen-Jun Xu. "Safety Early Warning Research for Highway Construction Based on Case-Based Reasoning and Variable Fuzzy Sets." Scientific World Journal 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/178954.

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As a high-risk subindustry involved in construction projects, highway construction safety has experienced major developments in the past 20 years, mainly due to the lack of safe early warnings in Chinese construction projects. By combining the current state of early warning technology with the requirements of the State Administration of Work Safety and using case-based reasoning (CBR), this paper expounds on the concept and flow of highway construction safety early warnings based on CBR. The present study provides solutions to three key issues, index selection, accident cause association analysis, and warning degree forecasting implementation, through the use of association rule mining, support vector machine classifiers, and variable fuzzy qualitative and quantitative change criterion modes, which fully cover the needs of safe early warning systems. Using a detailed description of the principles and advantages of each method and by proving the methods’ effectiveness and ability to act together in safe early warning applications, effective means and intelligent technology for a safe highway construction early warning system are established.

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Huang, Chao, Quan Yi Huang, Shao Bo Zhong, and Jian Guo Chen. "Case Reuse Based on Fuzzy Reasoning – Adaption for Emergency Management." Applied Mechanics and Materials 333-335 (July 2013): 1324–27. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1324.

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Emergency management is such a domain where experiential knowledge could be easily collected, and is quite suitable for the application of case based reasoning. However, in practice there are two problems limiting the effectiveness of CBR, the he incomplete information and changing situations. This paper proposed an approach based on fuzzy sets and text mining to solve those two problems, which contains four steps: a) represent the attributes with fuzzy sets, b) extract solution texts with text classification, c) establish connections of attributes and solutions with association rules, and d) adjust the solution with fuzzy reasoning. An example shows the adaption for emergency management and illustrates the improvement for CBR with the approach.

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Lau, Henry, C. K. M. Lee, Dilupa Nakandala, and Paul Shum. "An outcome-based process optimization model using fuzzy-based association rules." Industrial Management & Data Systems 118, no. 6 (July 9, 2018): 1138–52. http://dx.doi.org/10.1108/imds-08-2017-0347.

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Purpose The purpose of this paper is to propose an outcome-based process optimization model which can be deployed in companies to enhance their business operations, strengthening their competitiveness in the current industrial environment. To validate the approach, a case example has been included to assess the practicality and validity of this approach to be applied in actual environment. Design/methodology/approach This model embraces two approaches including: fuzzy logic for mimicking the human thinking and decision making mechanism; and data mining association rules approach for optimizing the analyzed knowledge for future decision-making as well as providing a mechanism to apply the obtained knowledge to support the improvement of different types of processes. Findings The new methodology of the proposed algorithm has been evaluated in a case study and the algorithm shows its potential to determine the primary factors that have a great effect upon the final result of the entire operation comprising a number of processes. In this case example, relevant process parameters have been identified as the important factors causing significant impact on the result of final outcome. Research limitations/implications The proposed methodology requires the dependence on human knowledge and personal experience to determine the various fuzzy regions of the processes. This can be fairly subjective and even biased. As such, it is advisable that the development of artificial intelligence techniques to support automatic machine learning to derive the fuzzy sets should be promoted to provide more reliable results. Originality/value Recent study on the relevant topics indicates that an intelligent process optimization approach, which is able to interact seamlessly with the knowledge-based system and extract useful information for process improvement, is still seen as an area that requires more study and investigation. In this research, the process optimization system with an effective process mining algorithm embedded for supporting knowledge discovery is proposed for use to achieve better quality control.

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Bellin, Nicolò, Erica Racchetti, Catia Maurone, Marco Bartoli, and Valeria Rossi. "Unsupervised Machine Learning and Data Mining Procedures Reveal Short Term, Climate Driven Patterns Linking Physico-Chemical Features and Zooplankton Diversity in Small Ponds." Water 13, no. 9 (April 28, 2021): 1217. http://dx.doi.org/10.3390/w13091217.

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Machine Learning (ML) is an increasingly accessible discipline in computer science that develops dynamic algorithms capable of data-driven decisions and whose use in ecology is growing. Fuzzy sets are suitable descriptors of ecological communities as compared to other standard algorithms and allow the description of decisions that include elements of uncertainty and vagueness. However, fuzzy sets are scarcely applied in ecology. In this work, an unsupervised machine learning algorithm, fuzzy c-means and association rules mining were applied to assess the factors influencing the assemblage composition and distribution patterns of 12 zooplankton taxa in 24 shallow ponds in northern Italy. The fuzzy c-means algorithm was implemented to classify the ponds in terms of taxa they support, and to identify the influence of chemical and physical environmental features on the assemblage patterns. Data retrieved during 2014 and 2015 were compared, taking into account that 2014 late spring and summer air temperatures were much lower than historical records, whereas 2015 mean monthly air temperatures were much warmer than historical averages. In both years, fuzzy c-means show a strong clustering of ponds in two groups, contrasting sites characterized by different physico-chemical and biological features. Climatic anomalies, affecting the temperature regime, together with the main water supply to shallow ponds (e.g., surface runoff vs. groundwater) represent disturbance factors producing large interannual differences in the chemistry, biology and short-term dynamic of small aquatic ecosystems. Unsupervised machine learning algorithms and fuzzy sets may help in catching such apparently erratic differences.

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Kent, Ray. "Rethinking Data Analysis - Part Two: Some Alternatives to Frequentist Approaches." International Journal of Market Research 51, no. 2 (January 2009): 1–16. http://dx.doi.org/10.1177/147078530905100212.

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In ‘Rethinking data analysis – part one: the limitations of frequentist approaches'’ (Kent 2009) it was argued that standard, frequentist statistics were developed for purposes entirely other than for the analysis of survey data; when applied in this context, the assumptions being made and the limitations of the statistical procedures are commonly ignored. This paper examines ways of approaching the analysis of data sets that can be seen as viable alternatives. It reviews Bayesian statistics, configurational and fuzzy set analysis, association rules in data mining, neural network analysis, chaos theory and the theory of the tipping point. Each of these approaches has its own limitations and not one of them can or should be seen as a total replacement for frequentist approaches. Rather, they are alternatives that should be considered when frequentist approaches are not appropriate or when they do not seem to be adequate to the task of finding patterns in a data set.

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

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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 (December 31, 2017): 13–18. http://dx.doi.org/10.26438/ijsrcse/v5i6.1318.

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

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

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Nembhard, D. A., K. K. Yip, and C. A. Stifter. "Association Rule Mining in Developmental Psychology." International Journal of Applied Industrial Engineering 1, no. 1 (January 2012): 23–37. http://dx.doi.org/10.4018/ijaie.2012010103.

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Developmental psychology is the scientific study of progressive psychological changes that occur in human beings as they age. Some of the current methodologies used in this field to study developmental processes include Yule’s Q, state space grids, time series analysis, and lag analysis. The data collected in this field are often time-series-type data. Applying association rule mining in developmental psychology is a new concept that may have a number of potential benefits. In this paper, two sets of infant-mother interaction data sets are examined using association rule mining. Previous analyses of these data used conventional statistical techniques. However, they failed to capture the dynamic interactions between the infant-mother pair as well as other issues relating to the temporal characteristic of the data. Three approaches are proposed in this paper as candidate means of addressing some of the questions that remain from previous studies. The approaches used can be applied to association rule mining to extend its application to data sets in related fields.

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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 (January 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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A, Anitha, and Freeda Jebamalar.S. "Predicting Dengue Using Fuzzy Association Rule Mining." International Journal of Computer Trends and Technology 67, no. 3 (March 25, 2019): 72–74. http://dx.doi.org/10.14445/22312803/ijctt-v67i3p114.

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Zhong, Yong. "An Association Rule Mining Algorithm of Multidimensional Sets." Journal of Computer Research and Development 43 (2006): 2117. http://dx.doi.org/10.1360/crad20061213.

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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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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 (March 9, 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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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 (February 16, 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 (March 20, 2018): 2187–98. http://dx.doi.org/10.1007/s13042-018-0806-9.

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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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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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Crivei, L. M. "Incremental Relational Association Rule Mining of Educational Data Sets." Studia Universitatis Babeș-Bolyai Informatica 63, no. 2 (June 19, 2018): 102–17. http://dx.doi.org/10.24193/subbi.2018.2.07.

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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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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 (December 2013): 6928–37. http://dx.doi.org/10.1016/j.eswa.2013.06.025.

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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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Chen, Yu Ke, and Tai Xiang Zhao. "Association Rule Mining Based on Multidimensional Pattern Relations." Advanced Materials Research 918 (April 2014): 243–45. http://dx.doi.org/10.4028/www.scientific.net/amr.918.243.

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Most incremental mining and online mining algorithms concentrate on finding association rules or patterns consistent with entire current sets of data. Users cannot easily obtain results from only interesting portion of data. This may prevent the usage of mining from online decision support for multidimensional data. To provide adhoc, query driven, and online mining support, we first propose a relation called the multidimensional pattern relation to structurally and systematically store context and mining information for later analysis.

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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 (April 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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Haraty, Ramzi A., and Rouba Nasrallah. "Indexing Arabic texts using association rule data mining." Library Hi Tech 37, no. 1 (March 18, 2019): 101–17. http://dx.doi.org/10.1108/lht-07-2017-0147.

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Purpose The purpose of this paper is to propose a new model to enhance auto-indexing Arabic texts. The model denotes extracting new relevant words by relating those chosen by previous classical methods to new words using data mining rules. Design/methodology/approach The proposed model uses an association rule algorithm for extracting frequent sets containing related items – to extract relationships between words in the texts to be indexed with words from texts that belong to the same category. The associations of words extracted are illustrated as sets of words that appear frequently together. Findings The proposed methodology shows significant enhancement in terms of accuracy, efficiency and reliability when compared to previous works. Research limitations/implications The stemming algorithm can be further enhanced. In the Arabic language, we have many grammatical rules. The more we integrate rules to the stemming algorithm, the better the stemming will be. Other enhancements can be done to the stop-list. This is by adding more words to it that should not be taken into consideration in the indexing mechanism. Also, numbers should be added to the list as well as using the thesaurus system because it links different phrases or words with the same meaning to each other, which improves the indexing mechanism. The authors also invite researchers to add more pre-requisite texts to have better results. Originality/value In this paper, the authors present a full text-based auto-indexing method for Arabic text documents. The auto-indexing method extracts new relevant words by using data mining rules, which has not been investigated before. The method uses an association rule mining algorithm for extracting frequent sets containing related items to extract relationships between words in the texts to be indexed with words from texts that belong to the same category. The benefits of the method are demonstrated using empirical work involving several Arabic texts.

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He, Xu, Fan Min, and William Zhu. "Parametric Rough Sets with Application to Granular Association Rule Mining." Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/461363.

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Granular association rules reveal patterns hidden in many-to-many relationships which are common in relational databases. In recommender systems, these rules are appropriate for cold-start recommendation, where a customer or a product has just entered the system. An example of such rules might be “40% men like at least 30% kinds of alcohol; 45% customers are men and 6% products are alcohol.” Mining such rules is a challenging problem due to pattern explosion. In this paper, we build a new type of parametric rough sets on two universes and propose an efficient rule mining algorithm based on the new model. Specifically, the model is deliberately defined such that the parameter corresponds to one threshold of rules. The algorithm benefits from the lower approximation operator in the new model. Experiments on two real-world data sets show that the new algorithm is significantly faster than an existing algorithm, and the performance of recommender systems is stable.

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COENEN, FRANS, and PAUL LENG. "Partitioning strategies for distributed association rule mining." Knowledge Engineering Review 21, no. 1 (March 2006): 25–47. http://dx.doi.org/10.1017/s0269888906000786.

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In this paper a number of alternative strategies for distributed/parallel association rule mining are investigated. The methods examined make use of a data structure, the T-tree, introduced previously by the authors as a structure for organizing sets of attributes for which support is being counted. We consider six different approaches, representing different ways of parallelizing the basic Apriori-T algorithm that we use. The methods focus on different mechanisms for partitioning the data between processes, and for reducing the message-passing overhead. Both ‘horizontal’ (data distribution) and ‘vertical’ (candidate distribution) partitioning strategies are considered, including a vertical partitioning algorithm (DATA-VP) which we have developed to exploit the structure of the T-tree. We present experimental results examining the performance of the methods in implementations using JavaSpaces. We conclude that in a JavaSpaces environment, candidate distribution strategies offer better performance than those that distribute the original dataset, because of the lower messaging overhead, and the DATA-VP algorithm produced results that are especially encouraging.

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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 (April 30, 2013): 399–406. http://dx.doi.org/10.4156/jcit.vol8.issue8.48.

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YU, LIGUO, and STEPHEN R. SCHACH. "APPLYING ASSOCIATION MINING TO CHANGE PROPAGATION." International Journal of Software Engineering and Knowledge Engineering 18, no. 08 (December 2008): 1043–61. http://dx.doi.org/10.1142/s0218194008004008.

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A software system evolves as changes are made to accommodate new features and repair defects. Software components are frequently interdependent, so changes made to one component can result in changes having to be made to other components to ensure that the system remains consistent; this is called change propagation. Accurate detection of change propagation is essential for software maintenance, which can be aided by accurate prediction of change propagation. In this paper, we study change propagation in three leading open-source software products: Linux, FreeBSD, and Apache HTTP Server. We use association rules-based data-mining techniques to detect change-propagation rules from the product version history. These rules are evaluated with respect to different training data sets and different test data sets. We discuss the applicability of using association-rule mining for change propagation, and several related issues. We find that a challenging issue in association-rule mining, concept drift, exists in software systems. Concept drift complicates the task of change-propagation prediction and requires special approaches, different from currently-used techniques for predicting change propagation.

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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 (November 30, 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 (September 30, 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 (December 2016): 249–54. http://dx.doi.org/10.1007/s40012-016-0119-7.

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Journal articles on the topic 'Association rule mining. Fuzzy sets'

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