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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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Kumar, Manoj, and Hemant Kumar Soni. "A Comparative Study of Tree-Based and Apriori-Based Approaches for Incremental Data Mining." International Journal of Engineering Research in Africa 23 (April 2016): 120–30. http://dx.doi.org/10.4028/www.scientific.net/jera.23.120.
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Association rule mining is an iterative and interactive process of discovering valid, novel, useful, understandable and hidden associations from the massive database. The Colossal databases require powerful and intelligent tools for analysis and discovery of frequent patterns and association rules. Several researchers have proposed the many algorithms for generating item sets and association rules for discovery of frequent patterns, and minning of the association rules. These proposals are validated on static data. A dynamic database may introduce some new association rules, which may be interesting and helpful in taking better business decisions. In association rule mining, the validation of performance and cost of the existing algorithms on incremental data are less explored. Hence, there is a strong need of comprehensive study and in-depth analysis of the existing proposals of association rule mining. In this paper, the existing tree-based algorithms for incremental data mining are presented and compared on the baisis of number of scans, structure, size and type of database. It is concluded that the Can-Tree approach dominates the other algorithms such as FP-Tree, FUFP-Tree, FELINE Alorithm with CATS-Tree etc.This study also highlights some hot issues and future research directions. This study also points out that there is a strong need for devising an efficient and new algorithm for incremental data mining.
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Pandey, Sachin. "Multilevel Association Rules in Data Mining." Journal of Advances and Scholarly Researches in Allied Education 15, no. 5 (2018): 74–78. http://dx.doi.org/10.29070/15/57517.
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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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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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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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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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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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Sonia M, Delphin, John Robinson P, and Sebastian Rajasekaran A. "Mining Efficient Fuzzy Bio-Statistical Rules for Association of Sandalwood in Pachaimalai Hills." International Journal of Agricultural and Environmental Information Systems 6, no. 2 (2015): 40–76. http://dx.doi.org/10.4018/ijaeis.2015040104.
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The integration of association rules and correlation rules with fuzzy logic can produce more abstract and flexible patterns for many real life problems, since many quantitative features in real world, especially surveying the frequency of plant association in any region is fuzzy in nature. This paper presents a modification of a previously reported algorithm for mining fuzzy association and correlation rules, defines the concept of fuzzy partial and semi-partial correlation rule mining, and presents an original algorithm for mining fuzzy data based on correlation rule mining. It adds a regression model to the procedure for mining fuzzy correlation rules in order to predict one data instance from contributing more than others. It also utilizes statistical analysis for the data and the experimental results show a very high utility of fuzzy association rules and fuzzy correlation rule mining in modeling plant association problems. The newly proposed algorithm is utilized for seeking close associations and relationships between a group of plant species clustering around Sandalwood in Pachaimalai hills, Eastern Ghats, Tamilnadu.
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Anisya. "Data Mining Prediction of Oil Palm Fruit." JAIA - Journal of Artificial Intelligence and Applications 1, no. 2 (2021): 07–12. http://dx.doi.org/10.33372/jaia.v1i2.707.
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The development of information technology today is very meaningful for all circles. Currently, information technology has become a necessity in everyday life. The use of information technology is proven to facilitate human performance. Where the number of suppliers that supply palm oil fruit every year will affect the activities of companies engaged in palm oil production. So that currently the company needs a decision-making strategy in the procurement of oil palm fruit. Data mining is a technology that is very useful to help companies find very important information from data centers. Data mining predicts trends and characteristics of business behavior which are very useful to support important decision making. One of the techniques the writer uses is the Association Rule technique. Association Rule is a data mining technique to find association rules between item combinations. Using the Association Rule technique will help companies predict which suppliers will supply palm fruit in the following year. Meanwhile, to predict the load does not use the association rule method but uses existing data analysis.
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Johan, Ragil Andika, Rispani Himilda, and Nadya Auliza. "PENERAPAN METODE ASSOCIATION RULE UNTUK STRATEGI PENJUALAN MENGGUNAKAN ALGORITMA APRIORI." Jurnal Teknik Informatika (J-Tifa) 2, no. 2 (2019): 1–7. http://dx.doi.org/10.52046/j-tifa.v2i2.268.
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Abstrak
 Persaingan dalam bisnis khususnya dalam bisnis perdagangan semakin banyak. Agar dapat meningkatkan penjualan produk yang dijual, para pelaku harus mempunyai strategi. Salah satu cara yang bisa dilakukan adalah dengan memanfaatkan data transaksi penjualan. Data penjualan tersebut dapat diolah hingga didapatkan informasi yang berguna bagi peningkatan penjualan. Teknologi yang dapat digunakan dalam hal ini adalah data mining. Data mining adalah kegiatan pengolahan data untuk menemukan hubungan dalam suatu data yang berjumlah besar. Suatu metode yang dapat digunakan dalam data mining adalah association rule mining. Association rule mining adalah salah satu metode data mining yang dapat mengidentifikasi hubungan kesamaan antar item. Algoritma yang paling sering dipakai dalam metode ini salah satunya ialah algoritma apriori. Algoritma apriori digunakan untuk mencari kandidat aturan asosiasi. Aturan kombinasi produk berhasil ditemukan dengan penerapan metode assosiation rules menggunakan algoritma apriori dan telah diuji menggunakan tools tanagra. Semua rule yang dihasilkan pada penelitian ini memiliki nilai lift ratio lebih dari 1 sehingga dapat digunakan sebagai acuan dalam membuat strategi penjualan.
 Kata Kunci : Penjualan, Data Mining, Association Rule, Algoritma Apriori
 
 Abstract
 Competition in business, especially in the trading business more and more. In order to increase sales of the products, businessman must have a strategy. A things we can do is to use sales transaction data. The sales data can be processed so we will get information of increasing sales. The technology that can be used in this case is data mining. Data mining, often also called knowledge discovery in database (KDD), is a data processing activity to find relationships in a large amount of data. A method that can be used in data mining is association rule mining. Association rule mining is one method of data mining that can identify the similarity relationships between items. One of the most frequently used algorithms in this method is the apriori algorithm. Apriori algorithm is used to find candidate association rules. The product combination rules have been found by applying the association rules method using apriori algorithm and have been tested using tanagra tools. All rules produced in this study have a lift ratio value of more than 1 so it can be used as a reference in making sales strategies.
 Keywords: Sale, Rule Mining, Association Rule, Apriori Algorithm
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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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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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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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Gandhimathi, D., and N. Anbazhagan. "Extracting of Positive and Negative Association Rules." International Journal of Emerging Research in Management and Technology 6, no. 8 (2018): 421. http://dx.doi.org/10.23956/ijermt.v6i8.175.
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Association rules analysis is a basic technique to expose how items/patterns are associated to each other. There are two common ways to measure association such as Support and Confidence. Several methods have been proposed in the literature to diminish the number of extracted association rules. Association Rule Mining is one of the greatest current data mining techniques designed to group objects together from huge databases aiming to take out the motivating correlation and relation with massive quantity of data. Association rule mining is used to discover the associated patterns from datasets. In this paper, we propose association rules from new methods on web usage mining. Generally, web usage log structure has several records so we have to overcome those unwanted records from large dataset. First of all the pre-processed data from the NASA dataset is clustered by the popular K-Means algorithm. Subsequently, the matrix calculation is progressed on that data. Further, the associations are performed on filtered data and get rid of the final associated page results. Positive and negative association rules are gathered by using new algorithm with Annul Object (𝒜𝒪). Wherever the object “𝒜𝒪” is presented those rules are known as negative association rule. Otherwise, the rules are positive association rules.
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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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B., Suma, and Shobha G. "Privacy preserving association rule hiding using border based approach." Indonesian Journal of Electrical Engineering and Computer Science 23, no. 2 (2021): 1137. http://dx.doi.org/10.11591/ijeecs.v23.i2.pp1137-1145.
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<div>Association rule mining is a well-known data mining technique used for extracting hidden correlations between data items in large databases. In the majority of the situations, data mining results contain sensitive information about individuals and publishing such data will violate individual secrecy. The challenge of association rule mining is to preserve the confidentiality of sensitive rules when releasing the database to external parties. The association rule hiding technique conceals the knowledge extracted by the sensitive association rules by modifying the database. In this paper, we introduce a border-based algorithm for hiding sensitive association rules. The main purpose of this approach is to conceal the sensitive rule set while maintaining the utility of the database and association rule mining results at the highest level. The performance of the algorithm in terms of the side effects is demonstrated using experiments conducted on two real datasets. The results show that the information loss is minimized without sacrificing the accuracy. </div>
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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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Mallik, Saurav, Anirban Mukhopadhyay, and Ujjwal Maulik. "Integrated Statistical and Rule-Mining Techniques for Dna Methylation and Gene Expression Data Analysis." Journal of Artificial Intelligence and Soft Computing Research 3, no. 2 (2013): 101–15. http://dx.doi.org/10.2478/jaiscr-2014-0008.
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Abstract For determination of the relationships among significant gene markers, statistical analysis and association rule mining are considered as very useful protocols. The first protocol identifies the significant differentially expressed/methylated gene markers, whereas the second one produces the interesting relationships among them across different types of samples or conditions. In this article, statistical tests and association rule mining based approaches have been used on gene expression and DNA methylation datasets for the prediction of different classes of samples (viz., Uterine Leiomyoma/class-formersmoker and uterine myometrium/class-neversmoker). A novel rule-based classifier is proposed for this purpose. Depending on sixteen different rule-interestingness measures, we have utilized a Genetic Algorithm based rank aggregation technique on the association rules which are generated from the training set of data by Apriori association rule mining algorithm. After determining the ranks of the rules, we have conducted a majority voting technique on each test point to estimate its class-label through weighted-sum method. We have run this classifier on the combined dataset using 4-fold cross-validations, and thereafter a comparative performance analysis has been made with other popular rulebased classifiers. Finally, the status of some important gene markers has been identified through the frequency analysis in the evolved rules for the two class-labels individually to formulate the interesting associations among them.
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Agrawal, Ankit, and Alok Choudhary. "Association Rule Mining Based HotSpot Analysis on SEER Lung Cancer Data." International Journal of Knowledge Discovery in Bioinformatics 2, no. 2 (2011): 34–54. http://dx.doi.org/10.4018/jkdb.2011040103.
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The authors analyze the lung cancer data available from the SEER program with the aim of identifying hotspots using association rule mining techniques. A subset of 13 patient attributes from the SEER data were recently linked with the survival outcome using prediction models, which is used in this study for segmentation. The goal here is to identify characteristics of patient segments where average survival is significantly higher/lower than average survival across the entire dataset. Automated association rule mining techniques resulted in hundreds of rules, from which many redundant rules were manually removed based on domain knowledge. Further, association rule mining based hotspot analysis was also conducted for conditional survival patient data, i.e., in cases where patients have already survived for a year after diagnosis. The resulting rules conform with existing biomedical knowledge and provide interesting insights into lung cancer survival.
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Prahartiwi, Lusa Indah, and Wulan Dari. "Algoritma Apriori untuk Pencarian Frequent itemset dalam Association Rule Mining." PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic 7, no. 2 (2019): 143–52. http://dx.doi.org/10.33558/piksel.v7i2.1817.
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Abstract
 
 Over decades, retail chains and department stores have been selling their products without using the transactional data generated by their sales as a source of knowledge. Abundant data availability, the need for information (or knowledge) as a support for decision making to create business solutions, and infrastructure support in the field of information technology are the embryos of the birth of data mining technology. Association rule mining is a data mining method used to extract useful patterns between data items. In this research, the Apriori algorithm was applied to find frequent itemset in association rule mining. Data processing using Tanagra tools. The dataset used was the Supermarket dataset consisting of 12 attributes and 108.131 transaction. The experimental results obtained by association rules or rules from the combination of item-sets beer wine spirit-frozen foods and snack foods as a Frequent itemset with a support value of 15.489% and a confidence value of 83.719%. Lift ratio value obtained was 2.47766 which means that there were some benefits from the association rule or rules. 
 
 Keywords: Apriori, Association Rule Mining.
 
 Abstrak
 
 Selama beberapa dekade rantai ritel dan department store telah menjual produk mereka tanpa menggunakan data transaksional yang dihasilkan oleh penjualan mereka sebagai sumber pengetahuan. Ketersediaan data yang melimpah, kebutuhan akan informasi (atau pengetahuan) sebagai pendukung pengambilan keputusan untuk membuat solusi bisnis, dan dukungan infrastruktur di bidang teknologi informasi merupakan cikal-bakal dari lahirnya teknologi data mining. Data mining menemukan pola yang menarik dari database seperti association rule, correlations, sequences, classifier dan masih banyak lagi yang mana association rule adalah salah satu masalah yang paling popular. Association rule mining merupakan metode data mining yang digunakan untuk mengekstrasi pola yang bermanfaat di antara data barang. Pada penelitian ini diterapkan algoritma Apriori untuk pencarian frequent itemset dalam association rule mining. Pengolahan data menggunakan tools Tanagra. Dataset yang digunakan adalah dataset Supermarket yang terdiri dari 12 atribut dan 108.131 transaksi. Hasil eksperimen diperoleh aturan asosiasi atau rules dari kombinasi itemsets beer wine spirit-frozen foods dan snack foods sebagai Frequent itemset dengan nilai support sebesar 15,489% dan nilai confidence sebesar 83,719%. Nilai Lift ratio yang diperoleh sebesar 2,47766 yang artinya terdapat manfaat dari aturan asosiasi atau rules tersebut.
 
 Kata kunci: Apriori, Association rule mining
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Thabtah, Fadi, Suhel Hammoud, and Hussein Abdel-Jaber. "Parallel Associative Classification Data Mining Frameworks Based MapReduce." Parallel Processing Letters 25, no. 02 (2015): 1550002. http://dx.doi.org/10.1142/s0129626415500024.
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Associative classification (AC) is a research topic that integrates association rules with classification in data mining to build classifiers. After dissemination of the Classification-based Association Rule algorithm (CBA), the majority of its successors have been developed to improve either CBA's prediction accuracy or the search for frequent ruleitems in the rule discovery step. Both of these steps require high demands in processing time and memory especially in cases of large training data sets or a low minimum support threshold value. In this paper, we overcome the problem of mining large training data sets by proposing a new learning method that repeatedly transforms data between line and item spaces to quickly discover frequent ruleitems, generate rules, subsequently rank and prune rules. This new learning method has been implemented in a parallel Map-Reduce (MR) algorithm called MRMCAR which can be considered the first parallel AC algorithm in the literature. The new learning method can be utilised in the different steps within any AC or association rule mining algorithms which scales well if contrasted with current horizontal or vertical methods. Two versions of the learning method (Weka, Hadoop) have been implemented and a number of experiments against different data sets have been conducted. The ground bases of the comparisons are classification accuracy and time required by the algorithm for data initialization, frequent ruleitems discovery, rule generation and rule pruning. The results reveal that MRMCAR is superior to both current AC mining algorithms and rule based classification algorithms in improving the classification performance with respect to accuracy.
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Wei, Qunfeng, and Bin Qi. "Neutrosophic Fuzzy Association Rule Generation-Based Big Data Mining Analysis Algorithm." International Transactions on Electrical Energy Systems 2022 (September 25, 2022): 1–7. http://dx.doi.org/10.1155/2022/1446405.
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As a very common and classic big data (BD) mining algorithm, the association rule data mining (DM) algorithm is often used to determine the internal correlation between different items and set a certain threshold to determine the size of the correlation. However, the traditional association rule algorithm is more suitable for establishing Boolean association rules between different items of different types of data, and hardening the sharp boundaries of the data causes the performance of the association rules to decrease. In order to overcome this shortcoming of classic DM, this article introduces association rules, support and confidence, the Apriori algorithm and fuzzy association rules based on the neutrosophic fuzzy association rule (NFAR). This paper is based on the data set of the supermarket purchase goods database, by drawing a radar chart to describe the correlation between different goods and different item sets support, and confidence calculation based on association rules support. Finally, the association rules are generated. Compared to the results produced by NFAR and ordinary association rules, the accuracy of the NFAR association rules algorithm in small data sets is 88.48%, while the accuracy of traditional association rules algorithm is only 80.87%, nearly 8 percentage points higher. On large data sets, the prediction accuracy of the neutral fuzzy association rules algorithm is 95.68%, while that of the traditional method is only 89.63%. Therefore, the NFAR algorithm can improve the accuracy and effectiveness of DM. This algorithm has great application prospects and development space in big DM and analysis.
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XU, YUE, and YUEFENG LI. "MINING NON-REDUNDANT ASSOCIATION RULES BASED ON CONCISE BASES." International Journal of Pattern Recognition and Artificial Intelligence 21, no. 04 (2007): 659–75. http://dx.doi.org/10.1142/s0218001407005600.
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Association rule mining has many achievements in the area of knowledge discovery. However, the quality of the extracted association rules has not drawn adequate attention from researchers in data mining community. One big concern with the quality of association rule mining is the size of the extracted rule set. As a matter of fact, very often tens of thousands of association rules are extracted among which many are redundant, thus useless. In this paper, we first analyze the redundancy problem in association rules and then propose a reliable exact association rule basis from which more concise nonredundant rules can be extracted. We prove that the redundancy eliminated using the proposed reliable association rule basis does not reduce the belief to the extracted rules. Moreover, this paper proposes a level wise approach for efficiently extracting closed itemsets and minimal generators — a key issue in closure based association rule mining.
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Shamie, Mohamad Mohamad, and Muhammad Mazen Almustafa. "Improving Association Rule Mining Using Clustering-Based Data Mining Model for Traffic Accidents." Review of Computer Engineering Studies 8, no. 3 (2021): 65–70. http://dx.doi.org/10.18280/rces.080301.
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Data mining is a process of knowledge discovery to extract the interesting, previously unknown, potentially useful, and nontrivial patterns from large data sets. Currently, there is an increasing interest in data mining in traffic accidents, which makes it a growing new research community. A large number of traffic accidents in recent years have generated large amounts of traffic accident data. The mining algorithms had a great role in determining the causes of these accidents, especially the association rule algorithms. One challenging problem in data mining is effective association rules mining with the huge transactional databases, many efforts have been made to propose and improve association rules mining methods. In the paper, we use the RapidMiner application to design a process that can generate association rules based on clustering algorithms.
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Kaur, Simranjit, and Seema Baghla. "Data Mining Approach in Retail Knowledge Discovery and Internet Technologies." Asian Journal of Engineering and Applied Technology 7, no. 2 (2018): 100–105. http://dx.doi.org/10.51983/ajeat-2018.7.2.998.
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Online shopping has a shopping channel or purchasing various items through online medium. Data mining is defined as a process used to extract usable data from a larger set of any raw data. The data set extraction from the demographic profiles and Questionnaire to investigate the gathered based by association. The method for shopping was totally changed with the happening to internet Technology. Association rule mining is one of the important problems of data mining has been used here. The goal of the association rule mining is to detect relationships or associations between specific values of categorical variables in large data sets.
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Ahluwalia, Madhu V., Aryya Gangopadhyay, and Zhiyuan Chen. "Preserving Privacy in Mining Quantitative Associations Rules." International Journal of Information Security and Privacy 3, no. 4 (2009): 1–17. http://dx.doi.org/10.4018/jisp.2009100101.
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Association rule mining is an important data mining method that has been studied extensively by the academic community and has been applied in practice. In the context of association rule mining, the state-of-the-art in privacy preserving data mining provides solutions for categorical and Boolean association rules but not for quantitative association rules. This article fills this gap by describing a method based on discrete wavelet transform (DWT) to protect input data privacy while preserving data mining patterns for association rules. A comparison with an existing kd-tree based transform shows that the DWT-based method fares better in terms of efficiency, preserving patterns, and privacy.
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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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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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Bicharra Garcia, Ana Cristina, Inhauma Ferraz, and Adriana S. Vivacqua. "From data to knowledge mining." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 23, no. 4 (2009): 427–41. http://dx.doi.org/10.1017/s089006040900016x.
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AbstractMost past approaches to data mining have been based on association rules. However, the simple application of association rules usually only changes the user's problem from dealing with millions of data points to dealing with thousands of rules. Although this may somewhat reduce the scale of the problem, it is not a completely satisfactory solution. This paper presents a new data mining technique, called knowledge cohesion (KC), which takes into account a domain ontology and the user's interest in exploring certain data sets to extract knowledge, in the form of semantic nets, from large data sets. The KC method has been successfully applied to mine causal relations from oil platform accident reports. In a comparison with association rule techniques for the same domain, KC has shown a significant improvement in the extraction of relevant knowledge, using processing complexity and knowledge manageability as the evaluation criteria.
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Grabusts, Pēteris. "APPLICATION POSSIBILITIES OF ASSOCIATION RULES IN STATISTICAL DATA ANALYSIS." Latgale National Economy Research 1, no. 6 (2014): 77. http://dx.doi.org/10.17770/lner2014vol1.6.1168.
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This paper studies one of intelligent data processing methods: using association rules for data analysis. The method of association rule obtaining what was initially developed to analyse consumer’s basket has turned to be a good tool for other tasks too. The method helps search and find regularities of the form X Y in different kinds of data. Nowadays this method is widely applied in the tasks of large scale database processing and analysing. As a result, methods of association rule construction occupy their place among the basic methods of intelligent data processing. The paper consists of two parts: theoretical and experimental. The theoretical part examines the mathematical aspects of association rule construction in detail and describes basic concepts and algorithm application possibilities. The experimental part presents implementation results and analysis of experiments. Conclusions have been drawn concerning the efficiency of association rules’ application in search of regularities. Even though the association rules mining method is among the fundamental data processing methods, in Latvia this method is not widely used, therefore, the article under consideration reveals the potential possibilities of the association rule mining in the analysis of statistical data.
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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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Gow, Jeremy, Simon Colton, Paul Cairns, and Paul Miller. "Mining Rules from Player Experience and Activity Data." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 8, no. 1 (2021): 148–53. http://dx.doi.org/10.1609/aiide.v8i1.12522.
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Feedback on player experience and behaviour can be invaluable to game designers, but there is need for specialised knowledge discovery tools to deal with high volume playtest data. We describe a study witha commercial third-person shooter, in which integrated player activity and experience data was captured and mined for design-relevant knowledge. We demonstrate that association rule learning and rule templates can be used to extractmeaningful rules relating player activity and experience during combat. We found that the number, type and quality of rules varies between experiences, and is affected by feature distributions. Further work is required on rule selection and evaluation.
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Kou, Zhicong. "Association rule mining using chaotic gravitational search algorithm for discovering relations between manufacturing system capabilities and product features." Concurrent Engineering 27, no. 3 (2019): 213–32. http://dx.doi.org/10.1177/1063293x19832949.
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An effective data mining method to automatically extract association rules between manufacturing capabilities and product features from the available historical data is essential for efficient and cost-effective product development and production. This article proposes a chaotic gravitational search algorithm–based association rule mining method for discovering the hidden relationship between manufacturing system capabilities and product features. The extracted rules would be utilized to predict capability requirements of various machines for the new product with different features. We use two strategies to incorporate chaos into gravitational search algorithm: one strategy is to embed chaotic map functions into the gravitational constant of gravitational search algorithm; the other is to use sequences generated by chaotic maps to substitute random numbers for different parameters of gravitational search algorithm. In order to improve the applicability of chaotic gravitational search algorithm–based association rule mining, a novel overlapping measure indication is further proposed to eliminate those unuseful rules. The proposed method is relatively simple and easy to implement. The rules generated by chaotic gravitational search algorithm–based association rule mining are accurate, interesting, and comprehensible to the user. The performance comparison indicates that chaotic gravitational search algorithm–based association rule mining outperforms other regular methods (e.g. Apriori) for association rule mining. The experimental results illustrate that chaotic gravitational search algorithm–based association rule mining is capable of discovering important association rules between manufacturing system capabilities and product features. This will help support planners and engineers for the new product design and manufacturing.
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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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Tan, Jun. "Different Types of Association Rules Mining Review." Applied Mechanics and Materials 241-244 (December 2012): 1589–92. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1589.
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In recent years, many application systems have generate large quantities of data, so it is no longer practical to rely on traditional database technique to analyze these data. Data mining offers tools for extracting knowledge from data, leading to significant improvement in the decision-making process. Association rules mining is one of the most important data mining technology. The paper first presents the basic concept of association rule mining, then discuss a few different types of association rules mining including multi-level association rules, multidimensional association rules, weighted association rules, multi-relational association rules, fuzzy association rules.
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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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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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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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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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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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Tan, Jun, and Ying Yong Bu. "Association Rules Mining in Manufacturing." Applied Mechanics and Materials 34-35 (October 2010): 651–54. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.651.
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In recent years, manufacturing processes have become more and more complex, manufacturing activities generate large quantities of data, so it is no longer practical to rely on traditional manual methods to analyze this data. Data mining offers tools for extracting knowledge from data, leading to significant improvement in the decision-making process. Association rules mining is one of the most important data mining techniques and has received considerable attention from researchers and practitioners. The paper presents the basic concept of association rule mining and reviews applications of association rules in manufacturing, including product design, manufacturing, process, customer relationship management, supply chain management, and product quality improvement. This paper is focused on demonstrating the relevancy of association rules mining to manufacturing industry, rather than discussing the association rules mining domain in general.
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Anitha, G., R. A. Karthika, G. Bindu, and G. V. Sriramakrishnan. "Modified classic apriori algorithm for association rule mining." International Journal of Engineering & Technology 7, no. 2.21 (2018): 414. http://dx.doi.org/10.14419/ijet.v7i2.21.12455.
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In today’s real world environment, information is the most critical element in all aspects of the life. It can be used to perform analysis and it helps to make decision making. But due to large collection of information the analysis and extraction of such useful information is tedious process which will create a major problem. In data mining, Association rules states about associations among the entities of known and unknown group and extracting hidden patterns in the data. Apriori algorithm is used for association rule mining. In this paper, due to limitations in rule condition, the algorithm was extended as new modified classic apriori algorithm which fulfills user stated minimum support and confidence constraints.
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Agarwal, Reshu, and Mandeep Mittal. "Inventory Classification Using Multi-Level Association Rule Mining." International Journal of Decision Support System Technology 11, no. 2 (2019): 1–12. http://dx.doi.org/10.4018/ijdsst.2019040101.
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Popular data mining methods support knowledge discovery from patterns that hold in relations. For many applications, it is difficult to find strong associations among data items at low or primitive levels of abstraction. Mining association rules at multiple levels may lead to more informative and refined knowledge from data. Multi-level association rule mining is a variation of association rule mining for finding relationships between items at each level by applying different thresholds at different levels. In this study, an inventory classification policy is provided. At each level, the loss profit of frequent items is determined. The obtained loss profit is used to rank frequent items at each level with respect to their category, content and brand. This helps inventory manager to determine the most profitable item with respect to their category, content and brand. An example is illustrated to validate the results. Further, to comprehend the impact of above approach in the real scenario, experiments are conducted on the exiting dataset.
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THABTAH, FADI, and SUHEL HAMMOUD. "MR-ARM: A MAP-REDUCE ASSOCIATION RULE MINING FRAMEWORK." Parallel Processing Letters 23, no. 03 (2013): 1350012. http://dx.doi.org/10.1142/s0129626413500126.
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Association rule is one of the primary tasks in data mining that discovers correlations among items in a transactional database. The majority of vertical and horizontal association rule mining algorithms have been developed to improve the frequent items discovery step which necessitates high demands on training time and memory usage particularly when the input database is very large. In this paper, we overcome the problem of mining very large data by proposing a new parallel Map-Reduce (MR) association rule mining technique called MR-ARM that uses a hybrid data transformation format to quickly finding frequent items and generating rules. The MR programming paradigm is becoming popular for large scale data intensive distributed applications due to its efficiency, simplicity and ease of use, and therefore the proposed algorithm develops a fast parallel distributed batch set intersection method for finding frequent items. Two implementations (Weka, Hadoop) of the proposed MR association rule algorithm have been developed and a number of experiments against small, medium and large data collections have been conducted. The ground bases of the comparisons are time required by the algorithm for: data initialisation, frequent items discovery, rule generation, etc. The results show that MR-ARM is very useful tool for mining association rules from large datasets in a distributed environment.
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Wang, Hui. "Hiding Sensitive Association Rules by Sanitizing." Advanced Materials Research 694-697 (May 2013): 2317–21. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.2317.
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The goal of knowledge discovery is to extract hidden or useful unknown knowledge from databases, while the objective of knowledge hiding is to prevent certain confidential data or knowledge from being extracted through data mining techniques. Hiding sensitive association rules is focused. The side-effects of the existing data mining technology are investigated. The problem of sensitive association rule hiding is described formally. The representative sanitizing strategies for sensitive association rule hiding are discussed.
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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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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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Petry, Frederick E. "Data Mining Approaches for Geo-Spatial Big Data." International Journal of Organizational and Collective Intelligence 3, no. 1 (2012): 52–71. http://dx.doi.org/10.4018/joci.2012010104.
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The availability of a vast amount of heterogeneous information from a variety of sources ranging from satellite imagery to the Internet has been termed as the problem of Big Data. Currently there is a great emphasis on the huge amount of geophysical data that has a spatial basis or spatial aspects. To effectively utilize such volumes of data, data mining techniques are needed to manage discovery from such volumes of data. An important consideration for this sort of data mining is to extend techniques to manage the inherent uncertainty involved in such spatial data. In this paper the authors first provide overviews of uncertainty representations based on fuzzy, intuitionistic, and rough sets theory and data mining techniques. To illustrate the issues they focus on the application of the discovery of association rules in approaches for vague spatial data. The extensions of association rule extraction for uncertain data as represented by rough and fuzzy sets are described. Finally an example of rule extraction for both fuzzy and rough set types of uncertainty representations is given
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YU, LIGUO, and STEPHEN R. SCHACH. "APPLYING ASSOCIATION MINING TO CHANGE PROPAGATION." International Journal of Software Engineering and Knowledge Engineering 18, no. 08 (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.