Journal articles: 'Rule mining'

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Taniar, David, Wenny Rahayu, Vincent Lee, and Olena Daly. "Exception rules in association rule mining." Applied Mathematics and Computation 205, no. 2 (November 2008): 735–50. http://dx.doi.org/10.1016/j.amc.2008.05.020.

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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 (December 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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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 (August 30, 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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Thomas, Binu, and G. Raju. "A Novel Web Classification Algorithm Using Fuzzy Weighted Association Rules." ISRN Artificial Intelligence 2013 (December 19, 2013): 1–10. http://dx.doi.org/10.1155/2013/316913.

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In associative classification method, the rules generated from association rule mining are converted into classification rules. The concept of association rule mining can be extended in web mining environment to find associations between web pages visited together by the internet users in their browsing sessions. The weighted fuzzy association rule mining techniques are capable of finding natural associations between items by considering the significance of their presence in a transaction. The significance of an item in a transaction is usually referred as the weight of an item in the transaction and finding associations between such weighted items is called fuzzy weighted association rule mining. In this paper, we are presenting a novel web classification algorithm using the principles of fuzzy association rule mining to classify the web pages into different web categories, depending on the manner in which they appear in user sessions. The results are finally represented in the form of classification rules and these rules are compared with the result generated using famous Boolean Apriori association rule mining algorithm.

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Adda, Mehdi, Rokia Missaoui, and Petko Valtchev. "Relation rule mining." International Journal of Parallel, Emergent and Distributed Systems 22, no. 6 (December 2007): 439–49. http://dx.doi.org/10.1080/17445760701207850.

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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 (June 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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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 (September 4, 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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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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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 (August 1, 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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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 (May 10, 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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Gopagoni, Praveen Kumar, and Mohan Rao S K. "Distributed elephant herding optimization for grid-based privacy association rule mining." Data Technologies and Applications 54, no. 3 (May 15, 2020): 365–82. http://dx.doi.org/10.1108/dta-07-2019-0104.

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PurposeAssociation rule mining generates the patterns and correlations from the database, which requires large scanning time, and the cost of computation associated with the generation of the rules is quite high. On the other hand, the candidate rules generated using the traditional association rules mining face a huge challenge in terms of time and space, and the process is lengthy. In order to tackle the issues of the existing methods and to render the privacy rules, the paper proposes the grid-based privacy association rule mining.Design/methodology/approachThe primary intention of the research is to design and develop a distributed elephant herding optimization (EHO) for grid-based privacy association rule mining from the database. The proposed method of rule generation is processed as two steps: in the first step, the rules are generated using apriori algorithm, which is the effective association rule mining algorithm. In general, the extraction of the association rules from the input database is based on confidence and support that is replaced with new terms, such as probability-based confidence and holo-entropy. Thus, in the proposed model, the extraction of the association rules is based on probability-based confidence and holo-entropy. In the second step, the generated rules are given to the grid-based privacy rule mining, which produces privacy-dependent rules based on a novel optimization algorithm and grid-based fitness. The novel optimization algorithm is developed by integrating the distributed concept in EHO algorithm.FindingsThe experimentation of the method using the databases taken from the Frequent Itemset Mining Dataset Repository to prove the effectiveness of the distributed grid-based privacy association rule mining includes the retail, chess, T10I4D100K and T40I10D100K databases. The proposed method outperformed the existing methods through offering a higher degree of privacy and utility, and moreover, it is noted that the distributed nature of the association rule mining facilitates the parallel processing and generates the privacy rules without much computational burden. The rate of hiding capacity, the rate of information preservation and rate of the false rules generated for the proposed method are found to be 0.4468, 0.4488 and 0.0654, respectively, which is better compared with the existing rule mining methods.Originality/valueData mining is performed in a distributed manner through the grids that subdivide the input data, and the rules are framed using the apriori-based association mining, which is the modification of the standard apriori with the holo-entropy and probability-based confidence replacing the support and confidence in the standard apriori algorithm. The mined rules do not assure the privacy, and hence, the grid-based privacy rules are employed that utilize the adaptive elephant herding optimization (AEHO) for generating the privacy rules. The AEHO inherits the adaptive nature in the standard EHO, which renders the global optimal solution.

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Mohd Khairudin, Nazli, Aida Mustapha, and Mohd Hanif Ahmad. "Effect of Temporal Relationships in Associative Rule Mining for Web Log Data." Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/813983.

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The advent of web-based applications and services has created such diverse and voluminous web log data stored in web servers, proxy servers, client machines, or organizational databases. This paper attempts to investigate the effect of temporal attribute in relational rule mining for web log data. We incorporated the characteristics of time in the rule mining process and analysed the effect of various temporal parameters. The rules generated from temporal relational rule mining are then compared against the rules generated from the classical rule mining approach such as the Apriori and FP-Growth algorithms. The results showed that by incorporating the temporal attribute via time, the number of rules generated is subsequently smaller but is comparable in terms of quality.

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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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Pal, Parashu Ram, Pankaj Pathak, and Shkurte Luma-Osmani. "IHAC: Incorporating Heuristics for Efficient Rule Generation & Rule Selection in Associative Classification." Journal of Information & Knowledge Management 20, no. 01 (March 2021): 2150010. http://dx.doi.org/10.1142/s0219649221500106.

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Associations rule mining along with classification rule mining are both significant techniques of mining of knowledge in the area of knowledge discovery in massive databases stored in different geographic locations of the world. Based on such combination of these two, class association rules for mining or associative classification methods have been generated, which, in far too many cases, showed higher prediction accuracy than platitudinous conventional classifiers. Motivated by the study, in this paper, we proposed a new approach, namely IHAC (Incorporating Heuristics for efficient rule generation & rule selection in Associative Classification). First, it utilises the database to decrease the search space and then explicitly explores the potent class association rules from the optimised database. This also blends rule generation and classifier building to speed up the overall classifier construction cycle. Experimental findings showed that IHAC performs better than any further associative classification methods.

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Wu, Xiaoxuan, Qiang Wen, and Jun Zhu. "Association rule mining with a special rule coding and dynamic genetic algorithm for air quality impact factors in Beijing, China." PLOS ONE 19, no. 3 (March 4, 2024): e0299865. http://dx.doi.org/10.1371/journal.pone.0299865.

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Understanding air quality requires a comprehensive understanding of its various factors. Most of the association rule techniques focuses on high frequency terms, ignoring the potential importance of low- frequency terms and causing unnecessary storage space waste. Therefore, a dynamic genetic association rule mining algorithm is proposed in this paper, which combines the improved dynamic genetic algorithm with the association rule mining algorithm to realize the importance mining of low- frequency terms. Firstly, in the chromosome coding phase of genetic algorithm, an innovative multi-information coding strategy is proposed, which selectively stores similar values of different levels in one storage unit. It avoids storing all the values at once and facilitates efficient mining of valid rules later. Secondly, by weighting the evaluation indicators such as support, confidence and promotion in association rule mining, a new evaluation index is formed, avoiding the need to set a minimum threshold for high-interest rules. Finally, in order to improve the mining performance of the rules, the dynamic crossover rate and mutation rate are set to improve the search efficiency of the algorithm. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the unit point multi-information coding strategy in reducing the rule storage air, the effectiveness of mining the rules formed by the low frequency item set, and the effectiveness of combining the rule mining algorithm with the swarm intelligence optimization algorithm in terms of search time and convergence. In the experimental stage, this paper adopts the 2016 annual air quality data set of Beijing to verify the effectiveness of the above three aspects. The unit point multi-information coding strategy reduced the rule space storage consumption by 50%, the new evaluation index can mine more interesting rules whose interest level can be up to 90%, while mining the rules formed by the lower frequency terms, and in terms of search time, we reduced it about 20% compared with some meta-heuristic algorithms, while improving convergence.

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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 (September 23, 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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BEAUSOLEIL, RICARDO P. "ASSOCIATIVE CLASSIFICATION WITH MULTIOBJECTIVE TABU SEARCH." Revista de Matemática: Teoría y Aplicaciones 27, no. 2 (June 23, 2020): 353–74. http://dx.doi.org/10.15517/rmta.v27i2.42438.

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This paper presents an application of Tabu Search algorithm to association rule mining. We focus our attention specifically on classification rule mining, often called associative classification, where the consequent part of each rule is a class label. Our approach is based on seek a rule set handled as an individual. A Tabu search algorithm is used to search for Pareto-optimal rule sets with respect to some evaluation criteria such as accuracy and complexity. We apply a called Apriori algorithm for an association rules mining and then a multiobjective tabu search to a selection rules. We report experimental results where the effect of our multiobjective selection rules is examined for some well-known benchmark data sets from the UCI machine learning repository.

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Tzacheva, Angelina A. "Rule schemas and interesting association action rules mining." International Journal of Data Mining, Modelling and Management 4, no. 3 (2012): 244. http://dx.doi.org/10.1504/ijdmmm.2012.048106.

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Thabtah, Fadi. "Rule Preference Effect in Associative Classification Mining." Journal of Information & Knowledge Management 05, no. 01 (March 2006): 13–20. http://dx.doi.org/10.1142/s0219649206001281.

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Classification based on association rule mining, also known as associative classification, is a promising approach in data mining that builds accurate classifiers. In this paper, a rule ranking process within the associative classification approach is investigated. Specifically, two common rule ranking methods in associative classification are compared with reference to their impact on accuracy. We also propose a new rule ranking procedure that adds more tie breaking conditions to the existing methods in order to reduce rule random selection. In particular, our method looks at the class distribution frequency associated with the tied rules and favours those that are associated with the majority class. We compare the impact of the proposed rule ranking method and two other methods presented in associative classification against 14 highly dense classification data sets. Our results indicate the effectiveness of the proposed rule ranking method on the quality of the resulting classifiers for the majority of the benchmark problems, which we consider. This provides evidence that adding more appropriate constraints to break ties between rules positively affects the predictive power of the resulting associative classifiers.

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Das, Madhabananda, Rahul Roy, Satchidananda Dehuri, and Sung-Bae Cho. "A New Approach to Associative Classification Based on Binary Multi-objective Particle Swarm Optimization." International Journal of Applied Metaheuristic Computing 2, no. 2 (April 2011): 51–73. http://dx.doi.org/10.4018/jamc.2011040103.

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Associative classification rule mining (ACRM) methods operate by association rule mining (ARM) to obtain classification rules from a previously classified data. In ACRM, classifiers are designed through two phases: rule extraction and rule selection. In this paper, the ACRM problem is treated as a multi-objective problem rather than a single objective one. As the problem is a discrete combinatorial optimization problem, it was necessary to develop a binary multi-objective particle swarm optimization (BMOPSO) to optimize the measure like coverage and confidence of association rule mining (ARM) to extract classification rules in rule extraction phase. In rule selection phase, a small number of rules are targeted from the extracted rules by BMOPSO to design an accurate and compact classifier which can maximize the accuracy of the rule sets and minimize their complexity simultaneously. Experiments are conducted on some of the University of California, Irvine (UCI) repository datasets. The comparative result of the proposed method with other standard classifiers confirms that the new proposed approach can be a suitable method for classification.

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

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Hidber, Christian. "Online association rule mining." ACM SIGMOD Record 28, no. 2 (June 1999): 145–56. http://dx.doi.org/10.1145/304181.304195.

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Ashmouni, Elhoussini F., Rabie A. Ramadan, and Ali A. Rashed. "Espresso for Rule Mining." Procedia Computer Science 32 (2014): 596–603. http://dx.doi.org/10.1016/j.procs.2014.05.465.

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Mashoria, Varsha, and Dr Anju Singh. "A Survey of Mining Association Rules Using Constraints." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 7, no. 3 (June 21, 2013): 620–25. http://dx.doi.org/10.24297/ijct.v7i3.3441.

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As we all know that association rule is used to find out the rules that are associated with the items present in the database that satisfy user specified support and confidence. There are many algorithms for mining association rules. For improving efficiency and effectiveness of mining task. Constraints based mining enable users to concentrate on mining interested association rules instead of the complete set of association rule.â€The constraints can be defined as the condition that a pattern has to satisfy â€ . This paper provides or gives the major advancement in the approaches for association rule mining using different constraints.

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Yang, Chaoyu, Jie Yang, and Zhenyu Yang. "Risk Factors Discovery for Cancer Survivability Analysis Using Graph-Rule Mining." Mathematical Problems in Engineering 2020 (July 31, 2020): 1–12. http://dx.doi.org/10.1155/2020/2384130.

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Mining and understanding patients’ disease-development pattern is a major healthcare need. A huge number of research studies have focused on medical resource allocation, survivability prediction, risk management of diagnosis, etc. In this article, we are specifically interested in discovering risk factors for patients with high probability of developing cancers. We propose a systematic and data-driven algorithm and build around the idea of association rule mining. More precisely, the rule-mining method is firstly applied on the target dataset to unpack the underlying relationship of cancer-risk factors, via generating a set of candidate rules. Later, this set is represented as a rule graph, where informative rules are identified and selected with the aim of enhancing the result interpretability. Compared to hundreds of rules generated from the standard rule-mining approach, the proposed algorithm benefits from a concise rule subset, without losing the information from the original rule set. The proposed algorithm is then evaluated using one of the largest cancer data resources. We found that our method outperforms existing approaches in terms of identifying informative rules and requires affordable computational time. Additionally, relevant information from the selected rules can also be used to inform health providers and authorities for cancer-risk management.

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Sonia M, Delphin, John Robinson P, and Sebastian Rajasekaran A. "Mining Efficient Fuzzy Bio-Statistical Rules for Association of Sandalwood in Pachaimalai Hills." International Journal of Agricultural and Environmental Information Systems 6, no. 2 (April 2015): 40–76. http://dx.doi.org/10.4018/ijaeis.2015040104.

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The integration of association rules and correlation rules with fuzzy logic can produce more abstract and flexible patterns for many real life problems, since many quantitative features in real world, especially surveying the frequency of plant association in any region is fuzzy in nature. This paper presents a modification of a previously reported algorithm for mining fuzzy association and correlation rules, defines the concept of fuzzy partial and semi-partial correlation rule mining, and presents an original algorithm for mining fuzzy data based on correlation rule mining. It adds a regression model to the procedure for mining fuzzy correlation rules in order to predict one data instance from contributing more than others. It also utilizes statistical analysis for the data and the experimental results show a very high utility of fuzzy association rules and fuzzy correlation rule mining in modeling plant association problems. The newly proposed algorithm is utilized for seeking close associations and relationships between a group of plant species clustering around Sandalwood in Pachaimalai hills, Eastern Ghats, Tamilnadu.

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Chen, Long, Jia Hua Liu, Qi Wang, Hua Sheng, and Yu Chen. "Design and Implement of Operational Rule Base Based on Machine Learning and Association Rule Mining." Applied Mechanics and Materials 734 (February 2015): 422–27. http://dx.doi.org/10.4028/www.scientific.net/amm.734.422.

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In order to ensure the security, stability and effective operation of information system, the construction and optimization techniques for information operational Rule Base has become an urgent problem to be solved. To meet the demands, this paper presents a rule base construction and optimization strategy based on machine learning and association rule mining. The operational rule base which includes basic rules, association rules and extension rules is generated by the network topology, the monitoring indicators and the association rule mining of historical data. Then implement machine learning method for rules to improve their performance. At last, the rule-upgrade strategy is proposed for rules to move from the lower region to higher region. Based on these steps, experimental results are given to verify the proposed strategy.

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

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

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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 (October 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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Abdelwahab, Amira, and Nesma Youssef. "Performance Evaluation of Sequential Rule Mining Algorithms." Applied Sciences 12, no. 10 (May 21, 2022): 5230. http://dx.doi.org/10.3390/app12105230.

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Data mining techniques are useful in discovering hidden knowledge from large databases. One of its common techniques is sequential rule mining. A sequential rule (SR) helps in finding all sequential rules that achieved support and confidence threshold for help in prediction. It is an alternative to sequential pattern mining in that it takes the probability of the following patterns into account. In this paper, we address the preferable utilization of sequential rule mining algorithms by applying them to databases with different features for improving the efficiency in different fields of application. The three compared algorithms are the TRuleGrowth algorithm, which is an extension sequential rule algorithm of RuleGrowth; the top-k non-redundant sequential rules algorithm (TNS); and a non-redundant dynamic bit vector (NRD-DBV). The analysis compares the three algorithms regarding the run time, the number of produced rules, and the used memory to nominate which of them is best suited in prediction. Additionally, it explores the most suitable applications for each algorithm to improve the efficiency. The experimental results proved that the performance of the algorithms appears related to the dataset characteristics. It has been demonstrated that altering the window size constraint, determining the number of created rules, or changing the value of the minSup threshold can reduce execution time and control the number of valid rules generated.

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Qian, Guoqi, Calyampudi Radhakrishna Rao, Xiaoying Sun, and Yuehua Wu. "Boosting association rule mining in large datasets via Gibbs sampling." Proceedings of the National Academy of Sciences 113, no. 18 (April 18, 2016): 4958–63. http://dx.doi.org/10.1073/pnas.1604553113.

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Current algorithms for association rule mining from transaction data are mostly deterministic and enumerative. They can be computationally intractable even for mining a dataset containing just a few hundred transaction items, if no action is taken to constrain the search space. In this paper, we develop a Gibbs-sampling–induced stochastic search procedure to randomly sample association rules from the itemset space, and perform rule mining from the reduced transaction dataset generated by the sample. Also a general rule importance measure is proposed to direct the stochastic search so that, as a result of the randomly generated association rules constituting an ergodic Markov chain, the overall most important rules in the itemset space can be uncovered from the reduced dataset with probability 1 in the limit. In the simulation study and a real genomic data example, we show how to boost association rule mining by an integrated use of the stochastic search and the Apriori algorithm.

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Prakash, R. Vijaya, S. S. V. N. Sarma, and M. Sheshikala. "Generating Non-redundant Multilevel Association Rules Using Min-max Exact Rules." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 6 (December 1, 2018): 4568. http://dx.doi.org/10.11591/ijece.v8i6.pp4568-4576.

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Association Rule mining plays an important role in the discovery of knowledge and information. Association Rule mining discovers huge number of rules for any dataset for different support and confidence values, among this many of them are redundant, especially in the case of multi-level datasets. Mining non-redundant Association Rules in multi-level dataset is a big concern in field of Data mining. In this paper, we present a definition for redundancy and a concise representation called Reliable Exact basis for representing non-redundant Association Rules from multi-level datasets. The given non-redundant Association Rules are loss less representation for any datasets.

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Wang, Guang Jiang, and Shi Guo Jin. "Application of Association Rule Mining Technology in Collection and Management of Wireless Sensor Network Node." Applied Mechanics and Materials 685 (October 2014): 575–78. http://dx.doi.org/10.4028/www.scientific.net/amm.685.575.

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Association rule mining is an important data mining method; it is the key link of finding frequent itemsets. The process of association rules mining is roughly into two steps: the first step is to find out from all the concentration of all the frequent itemsets; the second step is to obtain the association rules from frequent itemsets. This paper analyzes the collected information of nodes in wireless sensor network and management. The paper presents application of association rule mining technology in the collection and management of wireless sensor network node.

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Jain, Deepti, and Divakar Singh. "A Review on associative classification for Diabetic Datasets A Simulation Approach." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 7, no. 1 (May 21, 2013): 533–38. http://dx.doi.org/10.24297/ijct.v7i1.3483.

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Association rules are used to discover all the interesting relationship in a potentially large database. Association rule mining is used to discover a small set of rules over the database to form more accurate evaluation. They capture all possible rules that explain the presence of some attributes in relation to the presence of other attributes. This review paper aims to study and observe a flexible way, of, mining frequent patterns by extending the idea of the Associative Classification method. For better performance, the Neural Network Association Classification system is also analyzed here to be one of the approaches for building accurate and efficient classifiers. In this review paper, the Neural Network Association Classification system is studied and compared in order to find best possible accurate results. Association rule mining and classification rule mining can be integrated to form a framework called as Associative Classification and these rules are referred as Class Association Rules. This review research paper also analyzes how data mining techniques are used for predicting different types of diseases. This paper reviewed the research papers which mainly concentrated on predicting Diabetes.

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Varma, Sandeep, and LijiP I. "Secure Outsourced Association Rule Mining using Homomorphic Encryption." International Journal of Engineering Research and Science 3, no. 9 (September 30, 2017): 70–76. http://dx.doi.org/10.25125/engineering-journal-ijoer-sep-2017-22.

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G.Usha Rani, G. Usha Rani, R. Vijaya Prakash, and Prof A. Govardhan Prof. A. Govardhan. "Mining Multilevel Association Rule Using Pincer Search Algorithm." International Journal of Scientific Research 2, no. 5 (June 1, 2012): 54–57. http://dx.doi.org/10.15373/22778179/may2013/21.

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Ali, Nzar Abdulqader. "Finding minimum confidence threshold to avoid derived rules in association rule minin." Journal of Zankoy Sulaimani - Part A 17, no. 4 (August 30, 2015): 271–78. http://dx.doi.org/10.17656/jzs.10443.

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Ji, Lin, Hongyi Zhang, and Yue Zhang. "Domain-specific knowledge graph rule pattern mining based on generative adversarial networks." Applied and Computational Engineering 54, no. 1 (March 29, 2024): 219–25. http://dx.doi.org/10.54254/2755-2721/54/20241594.

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Most existing knowledge graphs (KGs) in specific domains suffer from problems of insufficient structural knowledge mining, superficial constraint of rules, incomplete system of rule patterns and higher error rate in the process of automated rule generation. In this paper, we present an adversarial generative approach for rule mining based on generative adversarial networks (GANs). The method firstly extracted a rule set according to a specific rule pattern defined manually, the rule set is then used as the adversarial training dataset for the GAN, That is, the discriminator determines whether a rule is true or not by learning the pattern of the rule set, and the generator tricks the discriminator by forging rules and improves according to the feedback from the generator.Finally, a generator is obtained to generate new rules that conform to the rule pattern, and a discriminator is obtained to determine the confidence of the automatically constructed triples.

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Shimada, Kaoru, and Takashi Hanioka. "An Evolutionary Method for Associative Contrast Rule Mining from Incomplete Database." Journal of Advanced Computational Intelligence and Intelligent Informatics 19, no. 6 (November 20, 2015): 766–77. http://dx.doi.org/10.20965/jaciii.2015.p0766.

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We propose a method for associative contrast rule mining from an incomplete database to find interesting differences between two incomplete datasets. The associative contrast rule is defined as follows: although an association rule “if X then Y” satisfies the given importance conditions within Database A, the same rule does not satisfy the same conditions within Database B. The proposed method extracts associative contrast rules directly without generating the frequent itemsets used in conventional rule mining methods. We developed our message using the basic evolutionary graph-based optimization basic structure and a new evolutionary strategy for rule accumulation mechanism. The method realizes association analysis between two classes of an incomplete database using the chi-square test. We evaluated the performance of the method for associative contrast rule mining from the incomplete database. Experimental results showed that our proposed method extracts associative contrast rules effectively. Evaluations of the mischief for rule measurements by missing values are demonstrated. Simulation results showed the difference between using the proposed method for an incomplete database and using the database as complete.

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Liu, Zhen Yu, Zhi Hui Song, Rui Qing Yan, and Zeng Zhang. "The Optimization Algorithm of Association Rules Mining." Applied Mechanics and Materials 614 (September 2014): 405–8. http://dx.doi.org/10.4028/www.scientific.net/amm.614.405.

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Frequent itemsets mining is the core part of association rule mining. At present most of the research on association rules mining is focused on how to improve the efficiency of mining frequent itemsets , however, the rule sets generated from frequent itemsets are the final results presented to decision makers for making, so how to optimize the rulesets generation process and the final rules is also worthy of attention. Based on encoding the dataset, this paper proposes a encoding method to speed up the generation process of frequent itemsets and proposes a subset tree to generate association rules which can simplify the generation process of rules and narrow the rulesets presented to decision makers.

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Hashemi, Shervin, and Pirooz Shamsinejad. "GA2RM: A GA-Based Action Rule Mining Method." International Journal of Computational Intelligence and Applications 20, no. 02 (June 2021): 2150012. http://dx.doi.org/10.1142/s1469026821500127.

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Action Mining is a subfield of Data Mining that tries to extract actions from traditional data sets. Action Rule is a type of rule that suggests some changes in its consequent part. Extracting action rules from data has been one of the research interests in recent years. Current state-of-the-art action rule mining methods like DEAR typically take classification rules as their input; Since traditional classification methods have been designed for prediction and not for manipulation, therefore extracting action rules directly from data can result in more valuable action rules. Here, we have proposed a method to generate action rules directly from data. To tackle the problem of huge search space of action rules, a Genetic Algorithm has been devised. Different metrics have been defined for investigating the effectiveness of our proposed method and a large number of experiments have been done on real and synthetic data sets. The results show that our method can find from 20% to 10 times more interesting (in case of support and confidence) action rules in comparison with its competitors.

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Abarajithan, Sivamathi, and S. Vijayarani Mohan. "Cockroach Swarm Optimization Algorithm for High Utility Association Rule Mining." International Journal of Swarm Intelligence Research 12, no. 3 (July 2021): 58–77. http://dx.doi.org/10.4018/ijsir.2021070103.

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Association rule mining is an important and widely used data mining technique. It is used to retrieve highly related objects in a database based on the occurrence. Recently, utility-based association rules were proposed to consider significant factors of the object. The main objective of this research work is to retrieve high utility association rules from a database using cockroach swarm optimization algorithm. So far, in the literature, no optimization algorithm was proposed in utility-based association rule mining. In this research work, CSOUAR (cockroach swarm optimization for high utility association rule mining) algorithm was proposed to generate utility association rules. CSOUAR algorithm is based on three behaviours of cockroach: chase-swarming, dispersing, and ruthless. To analyse the performance of CSOUAR, an improved particle swarm optimization (PSO-UAR), animal migration optimization (AMO-UAR), bees swarm optimisation (BSO-UAR), and penguins search optimisation (peSO-UAR) are also proposed in this work.

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Xu, Hong Sheng. "Construction Search Engine Based on Formal Concept Analysis and Association Rule Mining." Advanced Engineering Forum 6-7 (September 2012): 625–30. http://dx.doi.org/10.4028/www.scientific.net/aef.6-7.625.

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In the form of background in the form of concept partial relation to the corresponding concept lattice, concept lattice is the core data structure of formal concept analysis. Association rule mining process includes two phases: first find all the frequent itemsets in data collection, Second it is by these frequent itemsets to generate association rules. This paper analyzes the association rule mining algorithms, such as Apriori and FP-Growth. The paper presents the construction search engine based on formal concept analysis and association rule mining. Experimental results show that the proposed algorithm has high efficiency.

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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 (June 30, 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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THABTAH, FADI, and SUHEL HAMMOUD. "MR-ARM: A MAP-REDUCE ASSOCIATION RULE MINING FRAMEWORK." Parallel Processing Letters 23, no. 03 (September 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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Yin, Shao Hong, and Gui Dan Fan. "Research of POS Tagging Rules Mining Algorithm." Applied Mechanics and Materials 347-350 (August 2013): 2836–40. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2836.

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Part of speech contains important grammatical information, so it has great significance for the natural language understanding while the words in the sentence are marked on the parts of speech. POS tagging rules based on statistical methods and rule-based method can mining effectively, but its marked accuracy need to be improved. This paper presents a statistical method and rules of the combination of speech tagging rule mining algorithm in order to improve the correct rate of marked.

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Anuradha, C., and R. Anandavally. "Discovering Efficient Association Rule Mining via Correlation Analysis." Asian Journal of Computer Science and Technology 7, no. 1 (May 5, 2018): 46–49. http://dx.doi.org/10.51983/ajcst-2018.7.1.1831.

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A Discovery of Association rule mining is an essential task in Data Mining. Traditional approaches employ a support confidence framework for finding association rule. This leads to the exploration of a number of uninteresting rules, such rules are not interesting to the users. To tackle this weakness, this paper examines the correlation measures to augment with support and confidence framework, which resulting in the mining of correlation rules. We then added an additional interesting measure based on statistical significance and correlation analysis. This paper reveals an overview of interesting measures and gives an insight into the discovery of more meaningful rules from large applications than traditional approach. Also it covers a theoretical issues associated with correlations that have yet to be explored.

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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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La’biran, Roni. "Optimization of Association Rule Using Ant Colony Optimization (ACO) Approach." Wasit Journal of Computer and Mathematics Science 2, no. 3 (October 11, 2023): 99–106. http://dx.doi.org/10.31185/wjcms.190.

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The Apriori algorithm creates all possible association rules between items in the database using the Association Rule Mining and Apriori Algorithm. Using Ant Colony Optimization, a new algorithm is proposed for improving association rule mining results. Using ant colony behaviour as a starting point, an optimization of ant colonies (ACO) is developed. The Apriori algorithm creates association rules. Determine the weakest rule set and reduce the association rules to find rules of higher quality than apriori based on the Ant Colony algorithm's threshold value. Through optimization and improvement of rules generated for ACO, the proposed research work aims to reduce the scanning of datasets.

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Lu, Nannan, Shingo Mabu, and Kotaro Hirasawa. "Integrated Rule Mining Based on Fuzzy GNP and Probabilistic Classification for Intrusion Detection." Journal of Advanced Computational Intelligence and Intelligent Informatics 15, no. 5 (July 20, 2011): 495–505. http://dx.doi.org/10.20965/jaciii.2011.p0495.

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

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Journal articles on the topic 'Rule mining'

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