Journal articles: 'Data mining. Association rule mining. Economics'

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Haeri, Abdorrahman, and Reza Tavakkoli-Moghaddam. "DEVELOPING A HYBRID DATA MINING APPROACH BASED ON MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION FOR SOLVING A TRAVELING SALESMAN PROBLEM." Journal of Business Economics and Management 13, no. 5 (2012): 951–67. http://dx.doi.org/10.3846/16111699.2011.643445.

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A traveling salesman problem (TSP) is an NP-hard optimization problem. So it is necessary to use intelligent and heuristic methods to solve such a hard problem in a less computational time. This paper proposes a novel hybrid approach, which is a data mining (DM) based on multi-objective particle swarm optimization (MOPSO), called intelligent MOPSO (IMOPSO). The first step of the proposed IMOPSO is to find efficient solutions by applying the MOPSO approach. Then, the GRI (Generalized Rule Induction) algorithm, which is a powerful association rule mining, is used for extracting rules from efficient solutions of the MOPSO approach. Afterwards, the extracted rules are applied to improve solutions of the MOPSO for large-sized problems. Our proposed approach (IMOPSP) conforms to a standard data mining framework is called CRISP-DM and is performed on five standard problems with bi-objectives. The associated results of this approach are compared with the results obtained by the MOPSO approach. The results show the superiority of the proposed IMOPSO to obtain more and better solutions in comparison to the MOPSO approach.

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Babu, Mylam Chinnappan, and Sankaralingam Pushpa. "Protecting sensitive information utilizing an efficient association representative rule concealing algorithm for imbalance dataset." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 1 (2019): 527. http://dx.doi.org/10.11591/ijeecs.v15.i1.pp527-534.

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In data mining, discrimination is the detrimental behavior of the people which is extensively studied in human society and economical science. However, there are negative perceptions about the data mining. Discrimination has two categories; one is direct, and another is indirect. The decisions depend on sensitive information attributes are named as direct discrimination, and the decisions which depend on non-sensitive information attributes are called as indirect discrimination which is strongly related with biased sensitive ones. Privacy protection has become another one of the most important problems in data mining investigation. To overcome the above issues, an Efficient Association Representative Rule Concealing (EARRC) algorithm is proposed to protect sensitive information or knowledge and offer privacy protection with the classification of the sensitive data. Representative rule concealing is one kind of the privacy-preserving mechanisms to hide sensitive association rules. The objective of this paper is to reduce the alternation of the original database and perceive that there is no sensitive association rule is obtained. The proposed method hides the sensitive information by altering the database without modifying the support of the sensitive item. The EARRC is a type of association classification approach which integrates the benefits of both associative classification and rule-based PART (Projective Adaptive Resonance Theory) classification. Based on Experimental computations, proposed EARRC+PART classifier improves 1.06 NMI and 5.66 Accuracy compared than existing methodologies.

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Hussain, Sadiq, Neama Abdulaziz Dahan, Fadl Mutaher Ba-Alwi, and Najoua Ribata. "Educational Data Mining and Analysis of Students’ Academic Performance Using WEKA." Indonesian Journal of Electrical Engineering and Computer Science 9, no. 2 (2018): 447. http://dx.doi.org/10.11591/ijeecs.v9.i2.pp447-459.

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In this competitive scenario of the educational system, the higher education institutes use data mining tools and techniques for academic improvement of the student performance and to prevent drop out. The authors collected data from three colleges of Assam, India. The data consists of socio-economic, demographic as well as academic information of three hundred students with twenty-four attributes. Four classification methods, the J48, PART, Random Forest and Bayes Network Classifiers were used. The data mining tool used was WEKA. The high influential attributes were selected using the tool. The internal assessment attribute in the continuous evaluation process makes the highest impact in the final semester results of the students in our dataset. The results showed that random forest outperforms the other classifiers based on accuracy and classifier errors. Apriori algorithm was also used to find the association rule mining among all the attributes and the best rules were also displayed.

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Hashtarkhani, Soheil. "Extracting hidden rules from Brucellosis patients data in Razavi Khorasan province using association rule mining technique." Medical Technologies Journal 1, no. 4 (2017): 128. http://dx.doi.org/10.26415/2572-004x-vol1iss4p128-128.

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Brucellosis is a transmissible disease between humans and animals through infected animals and their products. The disease exists in most parts of the world especially in developing countries. Because of the serious impact of the disease in public health and socio-economical status, controlling the disease is very important in developing countries. The purpose of this article is to identify hidden patterns and relations between brucellosis patients which can be beneficial for physicians in diagnosis process. This study is a retrospective study of data collected from brucellosis patients of Razavi Khorasan province recorded at the health center, have been used. Due to differences in format and number of features collected during different years, before processing operations carried out in several stages to the same data. Fields associated with different methods and with expert opinion was converted into discrete fields and fields lost was estimated using the EM algorithm. APPIORI algorithm analysis was performed using the hidden relationships between data found that significant relationships were infected with expert opinion. Among the 163 relationship with over 7.0 Confidence rate which Weka software was discovered, by the application in consultation with an infectious disease expert, 10 clinically significant relationships were reported. Diagnosing brucellosis is really difficult to physicians because of its vague nature and symptoms. Because many unknown relationships between risk factors and demographic characteristics of the patients, the use of data mining concepts, especially in the medical data is beneficial because usually high volume assumptions are available. Further studies can test the validity of these rules like Randomize Control Trial studies.

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Huang, Xiaoling, Yangbing Xu, Shuai Zhang, and Wenyu Zhang. "Association Rule Mining for Selecting Proper Students to Take Part in Proper Discipline Competition: A Case Study of Zhejiang University of Finance and Economics." International Journal of Emerging Technologies in Learning (iJET) 13, no. 03 (2018): 100. http://dx.doi.org/10.3991/ijet.v13i03.8382.

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In recent years, the educational issues have attracted more and more researchers’ and teachers’ attention. On the other hand, the development of data mining technology, provides a new method to extract the useful information from the complex educational data. In order to increase the chance of students to be awarded in discipline competition, it is better to select the proper students to take part in the proper discipline competition. Therefore, in this study, we collect the information of 164 undergraduate students as a case study. All students majored in Software Engineering in Zhejiang University of Finance and Economics. The Apriori algorithm with group strategy is used to find the relationship between the students’ courses scores and competition awards. According to the results of association rule mining, we find that the students with higher scores of C# Development, Object-Oriented, Internet Web Design, Data Structure(C#), and Basic Programming will have a higher probability to be awarded in the competition.

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Tseng, Ming-Hseng, and Hui-Ching Wu. "Investigating Health Equity and Healthcare Needs among Immigrant Women Using the Association Rule Mining Method." Healthcare 9, no. 2 (2021): 195. http://dx.doi.org/10.3390/healthcare9020195.

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Equitable access to healthcare services is a major concern among immigrant women. Thus, this study investigated the relationship between socioeconomic characteristics and healthcare needs among immigrant women in Taiwan. The secondary data was obtained from “Survey of Foreign and Chinese Spouses’ Living Requirements, 2008”, which was administered to 5848 immigrant women by the Ministry of the Interior, Taiwan. Additionally, descriptive statistics and significance tests were used to analyze the data, after which the association rule mining algorithm was applied to determine the relationship between socioeconomic characteristics and healthcare needs. According to the findings, the top three healthcare needs were providing medical allowances (52.53%), child health checkups (16.74%), and parental knowledge and pre- and post-natal guidance (8.31%). Based on the association analysis, the main barrier to the women’s healthcare needs was “financial pressure”. This study also found that nationality, socioeconomic status, and duration of residence were associated with such needs, while health inequality among aged immigrant women was due to economic and physical factors. Finally, the association analysis found that the women’s healthcare problems included economic, socio-cultural, and gender weakness, while “economic inequality” and “women’s health” were interrelated.

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Schürer, Kevin, and Tatiana Penkova. "Creating a typology of parishes in England and Wales: Mining 1881 census data." Historical Life Course Studies 2 (September 29, 2015): 38–57. http://dx.doi.org/10.51964/hlcs9358.

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   The paper presents the application of principal component analysis and cluster analysis to historical individual level census data in order to explore social and economic variations and patterns in household structure across mid-Victorian England and Wales. Principal component analysis is used in order to identify and eliminate unimportant attributes within the data and the aggregation of the remaining attributes. By combining Kaiser’s rule and the Broken-stick model, four principal components are selected for subsequent data modelling. Cluster analysis is used in order to identify associations and structure within the data. A hierarchy of cluster structures is constructed with two, three, four and five clusters in 21-dimensional data space. The main differences between clusters are described in this paper.  

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Althuwaynee, Omar F., Ali Aydda, In-Tak Hwang, et al. "Uncertainty Reduction of Unlabeled Features in Landslide Inventory Using Machine Learning t-SNE Clustering and Data Mining Apriori Association Rule Algorithms." Applied Sciences 11, no. 2 (2021): 556. http://dx.doi.org/10.3390/app11020556.

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A landslide inventory, after an intense rainfall event in 1998, Southwestern Korea, was collected by digitizing aerial photographs. This left high uncertainty in the inventoried features to be verified by ground truths. To reduce the uncertainty, the photographs were reexamined, supported by the time slider in Google Earth. We observed 77 deformed slopes, which were similar in shape and texture, to the inventoried landslides. We then sought to label the observed formations based on their spatial relationship with surrounding conditions. A three-phase methodology was developed. First, an inventory of landslide, no landslide, vulnerable slopes, and unlabeled features was analyzed based on spatial cluster patterns, and then the dimension was reduced using the t-distributed stochastic neighbor embedding (t-SNE). Second, the Apriori algorithm, based on association rule mining, was used to identify common relations in the inventory using landslide antecedent factors (derived from topographic and landcover maps) that are linked to areas of unlabeled features. Third, the findings were validated using Landsat TM (Thematic mapper) and ETM+(Enhanced thematic mapper) images acquired before and after the original inventory. Current research offers practical and economical solutions (reduced reliance on paid remote sensing sensors and field survey) to labeling and classification of missing or outdated spatial attributed information.

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Althuwaynee, Omar F., Ali Aydda, In-Tak Hwang, et al. "Uncertainty Reduction of Unlabeled Features in Landslide Inventory Using Machine Learning t-SNE Clustering and Data Mining Apriori Association Rule Algorithms." Applied Sciences 11, no. 2 (2021): 556. http://dx.doi.org/10.3390/app11020556.

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A landslide inventory, after an intense rainfall event in 1998, Southwestern Korea, was collected by digitizing aerial photographs. This left high uncertainty in the inventoried features to be verified by ground truths. To reduce the uncertainty, the photographs were reexamined, supported by the time slider in Google Earth. We observed 77 deformed slopes, which were similar in shape and texture, to the inventoried landslides. We then sought to label the observed formations based on their spatial relationship with surrounding conditions. A three-phase methodology was developed. First, an inventory of landslide, no landslide, vulnerable slopes, and unlabeled features was analyzed based on spatial cluster patterns, and then the dimension was reduced using the t-distributed stochastic neighbor embedding (t-SNE). Second, the Apriori algorithm, based on association rule mining, was used to identify common relations in the inventory using landslide antecedent factors (derived from topographic and landcover maps) that are linked to areas of unlabeled features. Third, the findings were validated using Landsat TM (Thematic mapper) and ETM+(Enhanced thematic mapper) images acquired before and after the original inventory. Current research offers practical and economical solutions (reduced reliance on paid remote sensing sensors and field survey) to labeling and classification of missing or outdated spatial attributed information.

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Muhajir, Muhammad, and Berky Rian Efanna. "Association Rule Algorithm Sequential Pattern Discovery using Equivalent Classes (SPADE) to Analyze the Genesis Pattern of Landslides in Indonesia." International Journal of Advances in Intelligent Informatics 1, no. 3 (2015): 158. http://dx.doi.org/10.26555/ijain.v1i3.50.

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Landslide is one of movement of soil, rock, soil creep, and rock debris that occurred the move of the slopes. It is caused by steep slopes, high rainfall, deforestation, mining activities, and erosion. The impacts of the landslide are loss of property, damage to facilities such as homes and buildings, casualties, psychological trauma, disrupted economic and environmental damage. Based on the impacts of landslide, mitigation required to take early precautions are to know how the pattern of association between the sequence of events landslides and to know how the associative relationship pattern of earthquakes. Based on the impacts, the results of this research is associative relationship pattern is obtained from data flood that occurs in Indonesia, namely in case of heavy rain will occur labile soil structure to support the value of 0.37, confidence level of 41% and the power of formed ruled is 1.02.

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Yang, Jia, Keiichi Higuchi, Ryosuke Ando, and Yasuhide Nishihori. "Examining the Environmental, Vehicle, and Driver Factors Associated with Crossing Crashes of Elderly Drivers Using Association Rules Mining." Journal of Advanced Transportation 2020 (February 1, 2020): 1–8. http://dx.doi.org/10.1155/2020/2593410.

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In the aging society, reducing vehicle crashes caused by elderly drivers has become a crucial issue. To find effective methods to reduce these vehicle crashes, it is necessary to give some insights into the characteristics of vehicle crashes and those of traffic violations caused by elderly drivers. However, multiple significant factors associated with crossing crashes due to elderly drivers were not extensively observed in previous studies. To fill this research gap, this study identifies the crash pattern and examines the environmental, vehicle, and driver factors associated with crossing crashes due to elderly drivers. The 5-year crash data in Toyota City, Japan, are used for empirical analysis. The emerging data mining method called association rules mining is applied to discover various factors associated with crossing crashes of elderly and nonelderly drivers, respectively. The significant findings indicate that (1) elderly drivers are more likely to lead to crossing or right-turn crashes, compared with nonelderly drivers; (2) there are more factors including crash location (intersection without signal), lighting (daylight), road condition (dry and other), weather condition (clear and raining), vehicle type (light motor truck), and traffic violation (fail to confirm safety) associated with the large proportion of crossing crashes due to elderly drivers. The findings of this study can be used by traffic safety professionals to implement some countermeasures to reduce the crossing crashes due to elderly drivers.

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Kent, Ray. "Rethinking Data Analysis - Part Two: Some Alternatives to Frequentist Approaches." International Journal of Market Research 51, no. 2 (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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Guéguen, Philippe, and Alexandru Tiganescu. "Consideration of the Effects of Air Temperature on Structural Health Monitoring through Traffic Light-Based Decision-Making Tools." Shock and Vibration 2018 (August 29, 2018): 1–12. http://dx.doi.org/10.1155/2018/9258675.

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The real-time analysis of a structure’s integrity associated with a process to estimate damage levels improves the safety of people and assets and reduces the economic losses associated with interrupted production or operation of the structure. The appearance of damage in a building changes its dynamic response (frequency, damping, and/or modal shape), and one of the most effective methods for the continuous assessment of integrity is based on the use of ambient vibrations. However, although resonance frequency can be used as an indicator of change, misinterpretation is possible since frequency is affected not only by the occurrence of damage but also by certain operating conditions and particularly certain atmospheric conditions. In this study, after analyzing the correlation of resonance frequency values with temperature for one building, we use the data mining method called “association rule learning” (ARL) to predict future frequencies according to temperature measurements. We then propose an anomaly interpretation strategy using the “traffic light” method.

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Agrawal, Shivangee, and Nivedita Bairagi. "A Survey for Association Rule Mining in Data Mining." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 8 (2017): 245. http://dx.doi.org/10.23956/ijarcsse.v7i8.58.

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Data mining, also identified as knowledge discovery in databases has well-known its place as an important and significant research area. The objective of data mining (DM) is to take out higher-level unknown detail from a great quantity of raw data. DM has been used in a variety of data domains. DM can be considered as an algorithmic method that takes data as input and yields patterns, such as classification rules, itemsets, association rules, or summaries, as output. The ’classical’ associations rule issue manages the age of association rules by support portraying a base level of confidence and support that the roduced rules should meet. The most standard and classical algorithm used for ARM is Apriori algorithm. It is used for delivering frequent itemsets for the database. The essential thought behind this algorithm is that numerous passes are made the database. The total usage of association rule strategies strengthens the knowledge management process and enables showcasing faculty to know their customers well to give better quality organizations. In this paper, the detailed description has been performed on the Genetic algorithm and FP-Growth with the applications of the Association Rule Mining.

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Hong, Jungyeol, Reuben Tamakloe, and Dongjoo Park. "Discovering Insightful Rules among Truck Crash Characteristics using Apriori Algorithm." Journal of Advanced Transportation 2020 (January 16, 2020): 1–16. http://dx.doi.org/10.1155/2020/4323816.

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This study aims to discover hidden patterns and potential relationships in risk factors in freight truck crash data. Existing studies mainly used parametric models to analyze the causes of freight vehicle crashes. However, predetermined assumptions and underlying relationships between independent and dependent variables have been cited as its limitations. To overcome these limitations and provide a better understanding of factors that lead to truck crashes on the expressways, we applied the Association Rules Mining (ARM) technique, which is a nonparametric method. ARM quantifies the interrelationships between the antecedents and consequents of truck-involved crashes and provides researchers with the most influential set of factors that leads to crashes. We utilized a freight vehicle-involved crash data consisting of 19,038 crashes that occurred on the Korean expressways from 2008 to 2017 for this investigation. From the data, 90,951 association rules were generated through ARM employing the Apriori algorithm. The lift values estimated by the Apriori algorithm showed the strength of association between risk factors, and based on the estimated lift values, we identified key crash contributory factors that lead to truck-involved crashes at various segment types, under different weather conditions, considering the driver’s age, crash type, driver’s faults, vehicle size, and roadway geometry type. From the generated rules, we demonstrated that overspeeding with medium-weight trucks was highly associated with crashes during the rainy weather, whereas drowsy driving during the evening was correlated with crashes during fine weather. Segment-related crashes were mainly associated with driver’s faults and roadway geometry. Our results present useful insights and suggestions that can be used by transport stakeholders, including policymakers and researchers, to create relevant policies that will help reduce freight truck crashes on the expressways.

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Aly Abd Elaty, Amr, Rashed Salem, and Hatem Abdel Kader. "Efficient streaming data association rule mining." النشرة المعلوماتیة فی الحاسبات والمعلومات 1, no. 1 (2019): 35–41. http://dx.doi.org/10.21608/fcihib.2019.107515.

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Yatinkumar Kantilal, Solanki, and Yogesh Kumar Sharma. "UNDERSTANDING ASSOCIATION RULE IN DATA MINING." International Journal of Advanced Research 8, no. 6 (2020): 289–92. http://dx.doi.org/10.21474/ijar01/11097.

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Wang, Haixing, Yuanlanduo Tian, and Hong Yin. "Correlation Analysis of External Environment Risk Factors for High-Speed Railway Derailment Based on Unstructured Data." Journal of Advanced Transportation 2021 (July 19, 2021): 1–11. http://dx.doi.org/10.1155/2021/6980617.

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In railway operation, unsafe events such as faults may occur, and a large number of unsafe event records are generated in the process of unsafe events’ recording and reporting. Unsafe events have been described in unstructured natural language, which often has inconsistent structure and complex sources, involving multiple railway specialties, with multisource, heterogeneous, and unstructured characteristics. In practical application, the efficiency of processing is extremely low, leading to potentially unsafe management utilization. Based on the data on unsafe events, this paper utilizes big data processing technology, conducts association rules mining and association degree analysis, extracts the word segmentation, and obtains the feature vector of unsafe fault event data. At the same time, the unsafe event data analysis model is constructed in combination with regular expression and pattern matching technology. This paper establishes the matching model of high-speed railway derailment-based external environment risk factors and applies it to the occurrence of unsafe events. This model could be utilized to analyze and excavate the link between external environment risk factors and the occurrence of unsafe events and carry out the automatic extraction of characteristic information such as risk possibility and consequence severity; hence, it has potential for identifying, with enhanced accuracy, high-risk factors that may lead to high-speed railway derailment. Based on this study, we could make full use of the unsafe event data, forecast the risk trend, and discover the law of high-speed railway derailment. This study introduces a viable approach to analyzing the unsafe event data, forecasting risk trend, and conceptualizing high-speed railway derailment. It could also enforce the accurate quantification of high-speed railway safety situation, refine the risk index and conduct in-depth analysis combined with the model, and effectively support the digitalization and intellectualization of high-speed railway operation safety.

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Abedjan, Ziawasch, and Felix Naumann. "Improving RDF Data Through Association Rule Mining." Datenbank-Spektrum 13, no. 2 (2013): 111–20. http://dx.doi.org/10.1007/s13222-013-0126-x.

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ÇALIŞKAN,Buket DOĞAN,Kazım YILDIZ,Abdulsamet AKTAŞ, Duygu. "CRIME DATA ANALYSIS WITH ASSOCIATION RULE MINING." International Periodical of Recent Technologies in Applied Engineering 2, no. 2 (2021): 42–50. http://dx.doi.org/10.29228/porta.1.

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Kulkarni, Ashwini Rajendra, and Dr Shivaji D. Mundhe. "Data Mining Technique: An Implementation of Association Rule Mining in Healthcare." IARJSET 4, no. 7 (2017): 62–65. http://dx.doi.org/10.17148/iarjset.2017.4710.

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Wang, Hui. "Association Rule: From Mining to Hiding." Applied Mechanics and Materials 321-324 (June 2013): 2570–73. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.2570.

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Data mining is to discover knowledge which is unknown and hidden in huge database and would be helpful for people understand the data and make decision better. Some knowledge discovered from data mining is considered to be sensitive that the holder of the database will not share because it might cause serious privacy or security problems. Privacy preserving data mining is to hide sensitive knowledge and it is becoming more and more important and attractive. Association rule is one class of the most important knowledge to be mined, so as sensitive association rule hiding. The side-effects of the existing data mining technology are investigated and the representative strategies of association rule hiding are discussed.

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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 (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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Navale, Geeta S., and Suresh N. Mali. "Survey on Privacy Preserving Association Rule Data Mining." International Journal of Rough Sets and Data Analysis 4, no. 2 (2017): 63–80. http://dx.doi.org/10.4018/ijrsda.2017040105.

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The progress in the development of data mining techniques achieved in the recent years is gigantic. The collative data mining techniques makes the privacy preserving an important issue. The ultimate aim of the privacy preserving data mining is to extract relevant information from large amount of data base while protecting the sensitive information. The togetherness in the information retrieval with privacy and data quality is crucial. A detailed survey of the present methodologies for the association rule data mining and a review of the state of art method for privacy preserving association rule mining is presented in this paper. An analysis is provided based on the association rule mining algorithm techniques, objective measures, performance metrics and results achieved. The metrics and the short comings of the various existing technologies are also analysed. Finally, the authors present various research issues which can be useful for the researchers to accomplish further research on the privacy preserving association rule data mining.

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RAHUL KUMAR, VIJ, KALRA PARVEEN, and JAWALKAR C.S. "DATA MINING APPROACH FOR ADVANCEMENT OF “ASSOCIATION RULE MINING” USING “R PROGRAMMING”." i-manager’s Journal on Software Engineering 10, no. 4 (2016): 6. http://dx.doi.org/10.26634/jse.10.4.6057.

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Guleria, Pratiyush, Akshay Sharma, and Manu Sood. "Web-Based Data Mining Tools : Performing Feedback Analysis and Association Rule Mining." International Journal of Data Mining & Knowledge Management Process 5, no. 6 (2015): 35–44. http://dx.doi.org/10.5121/ijdkp.2015.5603.

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Reynaldo, Jason, and David Boy Tonara. "Data Mining Application using Association Rule Mining ECLAT Algorithm Based on SPMF." MATEC Web of Conferences 164 (2018): 01019. http://dx.doi.org/10.1051/matecconf/201816401019.

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Data mining is an important research domain that currently focused on knowledge discovery database. Where data from the database are mined so that information can be generated and used effectively and efficiently by humans. Mining can be applied to the market analysis. Association Rule Mining (ARM) has become the core of data mining. The search space is exponential in the number of database attributes and with millions of database objects the problem of I/O minimization becomes paramount. To get the information and the data such as, observation of the master data storage systems and interviews were done. Then, ECLAT algorithm is applied to the open-source library SPMF. In this project, this application can perform data mining assisted by open source SPMF with determined writing format of transaction data. It successfully displayed data with 100 % success rate. The application can generate a new easier knowledge which can be used for marketing the product.

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Wang, Hui. "Strategies for Sensitive Association Rule Hiding." Applied Mechanics and Materials 336-338 (July 2013): 2203–6. http://dx.doi.org/10.4028/www.scientific.net/amm.336-338.2203.

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Data mining technologies are used widely while the side effects it incurred are concerned so seriously. Privacy preserving data mining is so important for data and knowledge security during data mining applications. Association rule extracted from data mining is one kind of the most popular knowledge. It is challenging to hide sensitive association rules extracted by data mining process and make less affection on non-sensitive rules and the original database. In this work, we focus on specific association rule automatic hiding. Novel strategies are proposed which are based on increasing the support of the left hand and decreasing the support of the right hand. Quality measurements for sensitive association rules hiding are presented.

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Zhao, Zhenyi, Zhou Jian, Gurjot Singh Gaba, Roobaea Alroobaea, Mehedi Masud, and Saeed Rubaiee. "An improved association rule mining algorithm for large data." Journal of Intelligent Systems 30, no. 1 (2021): 750–62. http://dx.doi.org/10.1515/jisys-2020-0121.

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Abstract The data with the advancement of information technology are increasing on daily basis. The data mining technique has been applied to various fields. The complexity and execution time are the major factors viewed in existing data mining techniques. With the rapid development of database technology, many data storage increases, and data mining technology has become more and more important and expanded to various fields in recent years. Association rule mining is the most active research technique of data mining. Data mining technology is used for potentially useful information extraction and knowledge from big data sets. The results demonstrate that the precision ratio of the presented technique is high comparable to other existing techniques with the same recall rate, i.e., the R-tree algorithm. The proposed technique by the mining effectively controls the noise data, and the precision rate is also kept very high, which indicates the highest accuracy of the technique. This article makes a systematic and detailed analysis of data mining technology by using the Apriori algorithm.

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Mohan, S. Vijayarani, and Tamilarasi Angamuthu. "Association Rule Hiding in Privacy Preserving Data Mining." International Journal of Information Security and Privacy 12, no. 3 (2018): 141–63. http://dx.doi.org/10.4018/ijisp.2018070108.

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This article describes how privacy preserving data mining has become one of the most important and interesting research directions in data mining. With the help of data mining techniques, people can extract hidden information and discover patterns and relationships between the data items. In most of the situations, the extracted knowledge contains sensitive information about individuals and organizations. Moreover, this sensitive information can be misused for various purposes which violate the individual's privacy. Association rules frequently predetermine significant target marketing information about a business. Significant association rules provide knowledge to the data miner as they effectively summarize the data, while uncovering any hidden relations among items that hold in the data. Association rule hiding techniques are used for protecting the knowledge extracted by the sensitive association rules during the process of association rule mining. Association rule hiding refers to the process of modifying the original database in such a way that certain sensitive association rules disappear without seriously affecting the data and the non-sensitive rules. In this article, two new hiding techniques are proposed namely hiding technique based on genetic algorithm (HGA) and dummy items creation (DIC) technique. Hiding technique based on genetic algorithm is used for hiding sensitive association rules and the dummy items creation technique hides the sensitive rules as well as it creates dummy items for the modified sensitive items. Experimental results show the performance of the proposed techniques.

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Xiaoyan Wan. "Research on Data Mining Technology of Association Rule." Journal of Convergence Information Technology 8, no. 6 (2013): 628–35. http://dx.doi.org/10.4156/jcit.vol8.issue6.75.

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Chen, Xiao-hong, Bang-chuan Lai, and Ding Luo. "Mining association rule efficiently based on data warehouse." Journal of Central South University of Technology 10, no. 4 (2003): 375–80. http://dx.doi.org/10.1007/s11771-003-0042-6.

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Son, Yoon-Ho, In-Kyu Kim, and Nam-Gyu Kim. "Automated Conceptual Data Modeling Using Association Rule Mining." Journal of Information Systems 18, no. 4 (2009): 59–86. http://dx.doi.org/10.5859/kais.2009.18.4.059.

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Haraty, Ramzi A., and Rouba Nasrallah. "Indexing Arabic texts using association rule data mining." Library Hi Tech 37, no. 1 (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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Jiang, Nan, and Le Gruenwald. "Research issues in data stream association rule mining." ACM SIGMOD Record 35, no. 1 (2006): 14–19. http://dx.doi.org/10.1145/1121995.1121998.

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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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COENEN, FRANS, and PAUL LENG. "Partitioning strategies for distributed association rule mining." Knowledge Engineering Review 21, no. 1 (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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H.Patil, Pritam, Suvarna Thube, Bhakti Ratnaparkhi, and K. Rajeswari. "Analysis of Different Data Mining Tools using Classification, Clustering and Association Rule Mining." International Journal of Computer Applications 93, no. 8 (2014): 35–39. http://dx.doi.org/10.5120/16238-5766.

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Sun, Hongxiang, Zhongkai Yao, and Qingchun Miao. "Design of Macroeconomic Growth Prediction Algorithm Based on Data Mining." Mobile Information Systems 2021 (September 2, 2021): 1–8. http://dx.doi.org/10.1155/2021/2472373.

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With the rapid development of information technology and globalization of economy, financial data are being generated and collected at an unprecedented rate. Consequently, there has been a dire need of automated methods for effective and proficient utilization of a substantial amount of financial data to help in investment planning and decision-making. Data mining methods have been employed to discover hidden patterns and estimate future tendencies in financial markets. In this article, an improved macroeconomic growth prediction algorithm based on data mining and fuzzy correlation analysis is presented. This study analyzes the sequence of economic characteristics, reorganizes the spatial structure of economic characteristics, and integrates the statistical information of economic data. Using the optimized Apriori algorithm, the association rules between macroeconomic data are generated. Distinct features are extracted according to association rules using the joint distribution characteristic quantity of macroeconomic time series. Moreover, the Doppler parameter of macroeconomic time series growth prediction is calculated, and the residual analysis method of the regression model is used to predict the growth of macroeconomic data. Experimental results show that the proposed algorithm has better adaptability, less computation time, and higher prediction accuracy of economic data mining.

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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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Ding, Qin, and William Perrizo. "Support-Less Association Rule Mining Using Tuple Count Cube." Journal of Information & Knowledge Management 06, no. 04 (2007): 271–80. http://dx.doi.org/10.1142/s0219649207001846.

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Association rule mining is one of the important tasks in data mining and knowledge discovery (KDD). The traditional task of association rule mining is to find all the rules with high support and high confidence. In some applications, we are interested in finding high confidence rules even though the support may be low. This type of problem differs from the traditional association rule mining problem; hence, it is called support-less association rule mining. Existing algorithms for association rule mining, such as the Apriori algorithm, cannot be used efficiently for support-less association rule mining since those algorithms mostly rely on identifying frequent item-sets with high support. In this paper, we propose a new model to perform support-less association rule mining, i.e., to derive high confidence rules regardless of their support level. A vertical data structure, the Peano Count Tree (P-tree), is used in our model to represent all the information we need. Based on the P-tree structure, we build a special data cube, called the Tuple Count Cube (T-cube), to derive high confidence rules. Data cube operations, such as roll-up, on T-cube, provide efficient ways to calculate the count information needed for support-less association rule mining.

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., Meenakshi, and Rainu Nandal. "A literature review: big data and association rule mining." International Journal of Engineering & Technology 7, no. 2.7 (2018): 948. http://dx.doi.org/10.14419/ijet.v7i2.7.11431.

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In Today’s modern and advanced era, huge amounts of data have become available on hand to developers and choice makers. Big data successfully handles datasets that are not only large, but also very high in velocity and variety, which difficult to handle using conventional techniques, methods and tools. Multilevel association rule mining plays a very vital role in distributed environment in analysis of big data for preparing different Marketing strategies. As compared to Single Level rule, more precise and prominent information is provided by multilevel association rule and additionally from the hierarchical dataset it generates the conceptual hierarchy of knowledge. This paper aims to analyze Data Mining Technique named Multilevel Association rule, which provides additional important information in comparison to single level rule, and it also invents conceptual hierarchy of information/data from the hierarchical dataset. Tools and techniques of Big Data have also been reviewed in detail.

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Duraiswamy, K., and N. Maheswari. "Sensitive Items in Privacy Preserving — Association Rule Mining." Journal of Information & Knowledge Management 07, no. 01 (2008): 31–35. http://dx.doi.org/10.1142/s0219649208001932.

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Privacy-preserving has recently been proposed in response to the concerns of preserving personal or sensible information derived from data-mining algorithms. For example, through data-mining, sensible information such as private information or patterns may be inferred from non-sensible information or unclassified data. As large repositories of data contain confidential rules that must be protected before published, association rule hiding becomes one of important privacy preserving data-mining problems. There have been two types of privacy concerning data-mining. Output privacy tries to hide the mining results by minimally altering the data. Input privacy tries to manipulate the data so that the mining result is not affected or minimally affected. For some applications certain sensitive predictive rules are hidden that contain given sensitive items. To identify the sensitive items an algorithm SENSITEM is proposed. The results of the work have been given.

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Rashid, Ayesha, Sana Asif, Naveed Anwer Butt, and Imran Ashraf. "Feature Level Opinion Mining of Educational Student Feedback Data using Sequential Pattern Mining and Association Rule Mining." International Journal of Computer Applications 81, no. 10 (2013): 31–38. http://dx.doi.org/10.5120/14050-2215.

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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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Kumar, K. Mohan, and S. Devi. "Computational Study on Association Rule Mining Using Microarray Data." International Journal of Computer Sciences and Engineering 6, no. 11 (2018): 299–303. http://dx.doi.org/10.26438/ijcse/v6i11.299303.

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

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Fan, Yang. "The Optimization of Association Rule Algorithm in Data Mining." Applied Mechanics and Materials 624 (August 2014): 549–52. http://dx.doi.org/10.4028/www.scientific.net/amm.624.549.

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Association rule algorithm is an important issue in data mining,and in recent years, it has been extensively studied by the industry.Association rules reflect the interdependence and correlation of a transaction with other things.According to the author's many years of experience, this paper proposes a new algorithm based on binary tree BT_CM gradually merge new accumulation principle.The application shows that the improved algorithm has the characteristics of simple, accurate test to improve the efficiency and accuracy of data mining requirements.

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S., Mrs Vasanthi, and Ms S. Nandhini S. "Privacy Preserving Using Association Rule in Data Mining Techniques." IJARCCE 4, no. 8 (2015): 491–92. http://dx.doi.org/10.17148/ijarcce.2015.48106.

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Abdel-Basset, Mohamed, Mai Mohamed, Florentin Smarandache, and Victor Chang. "Neutrosophic Association Rule Mining Algorithm for Big Data Analysis." Symmetry 10, no. 4 (2018): 106. http://dx.doi.org/10.3390/sym10040106.

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Journal articles on the topic 'Data mining. Association rule mining. Economics'

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