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Journal articles on the topic 'Association Rule Mining (ARM)'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

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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Abouzakhar, Nasser S., Huankai Chen, and Bruce Christianson. "An Enhanced Fuzzy ARM Approach for Intrusion Detection." International Journal of Digital Crime and Forensics 3, no. 2 (2011): 41–61. http://dx.doi.org/10.4018/jdcf.2011040104.

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The integration of fuzzy logic with data mining methods such as association rules has achieved interesting results in various digital forensics applications. As a data mining technique, the association rule mining (ARM) algorithm uses ranges to convert any quantitative features into categorical ones. Such features lead to the sudden boundary problem, which can be smoothed by incorporating fuzzy logic so as to develop interesting patterns for intrusion detection. This paper introduces a Fuzzy ARM-based intrusion detection model that is tested on the CAIDA 2007 backscatter network traffic dataset. Moreover, the authors present an improved algorithm named Matrix Fuzzy ARM algorithm for mining fuzzy association rules. The experiments and results that are presented in this paper demonstrate the effectiveness of integrating fuzzy logic with association rule mining in intrusion detection. The performance of the developed detection model is improved by using this integrated approach and improved algorithm.

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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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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 (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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P., Sampath. "Performance Analysis Of Web Page Prediction With Markov Model, Association Rule Mining(Arm) And Association Rule Mining With Statistical Features(Arm-Sf)." IOSR Journal of Computer Engineering 8, no. 5 (2013): 70–74. http://dx.doi.org/10.9790/0661-0857074.

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Dhenabayu, Riska, and Ryan Ahmad Syah Putra. "SALES FORECASTING OF PHARMACEUTICAL `PRODUCTS USING ASSOCIATION RULE MINING (ARM) METHOD." JOSAR (Journal of Students Academic Research) 2, no. 2 (2017): 1–16. http://dx.doi.org/10.35457/josar.v2i02.630.

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This forecasting application can be applied to the pharmaceutical industry including the PPNI Wlingi pharmacy, which sells a variety of over-the-counter and prescription drugs and medical devices. The purchase of over-the-counter drugs is recorded daily. This, if left unchecked, will caused an accumulate sales data without any utilization of these data. Instead, the data can be used to determine the inventory turn over of goods so that it is useful in future sales strategies. One method that can be used is the Association Rule Mining (ARM) method which implement association rules. Association rule can be used to predict patterns of interrelationship between items that are often purchased by customers. The result will make it easier for companies to make the decision to increase or decrease the stock so that the inventory turn over will be shortened and reduce the risk of innefficient accumulation of goods.

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A. A. Rashid, Rabiatul, Puteri N. E. Nohuddin, and Zuraini Zainol. "Analyzing Climate Variability in Malaysia Using Association Rule Mining." International Journal of Engineering & Technology 7, no. 4.34 (2018): 394. http://dx.doi.org/10.14419/ijet.v7i4.34.26881.

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Previous surveys proved that data mining is one of the methods that can be utilized for climate prediction, predominantly clustering and classification are the most applied methods in data mining to build a model to predict changes in the climate. Unlike the climate change, climate variability is a phenomenon where the occurrence of climate uncertainty is according to the changes year to year basis. This study is focusing to look at the effectiveness of the Association Rule Mining (ARM) techniques in predicting climate variability events in Malaysia. In this report, it explained how the patterns that exist within climate data is discovered using ARM and how the extracted pattern is used to predict climate variability. In this report also, a framework is developed to explain how ARM can generate rules and extract patterns from the data and how the extracted rules and patterns is used to develop a model for predicting climate variability event.

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8

Khoshahval, S., M. Farnaghi, and M. Taleai. "SPATIO-TEMPORAL PATTERN MINING ON TRAJECTORY DATA USING ARM." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4 (September 27, 2017): 395–99. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w4-395-2017.

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Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity patterns from GPS trajectories using association rules. Finding user patterns needs extraction of user’s visited places from stops and moves of GPS trajectories. In order to locate stops and moves, we have implemented a place recognition algorithm. After extraction of visited points an advanced association rule mining algorithm, called Apriori was used to extract user activity patterns. This study outlined that there are useful patterns in each trajectory that can be emerged from raw GPS data using association rule mining techniques in order to find out about multiple users’ behaviour in a system and can be utilized in various location-based applications.

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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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Lin, Lin, and Mei-Ling Shyu. "Weighted Association Rule Mining for Video Semantic Detection." International Journal of Multimedia Data Engineering and Management 1, no. 1 (2010): 37–54. http://dx.doi.org/10.4018/jmdem.2010111203.

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Semantic knowledge detection of multimedia content has become a very popular research topic in recent years. The association rule mining (ARM) technique has been shown to be an efficient and accurate approach for content-based multimedia retrieval and semantic concept detection in many applications. To further improve the performance of traditional association rule mining technique, a video semantic concept detection framework whose classifier is built upon a new weighted association rule mining (WARM) algorithm is proposed in this article. Our proposed WARM algorithm is able to capture the different significance degrees of the items (feature-value pairs) in generating the association rules for video semantic concept detection. Our proposed WARM-based framework first applies multiple correspondence analysis (MCA) to project the features and classes into a new principle component space and discover the correlation between feature-value pairs and classes. Next, it considers both correlation and percentage information as the measurement to weight the feature-value pairs and to generate the association rules. Finally, it performs classification by using these weighted association rules. To evaluate our WARM-based framework, we compare its performance of video semantic concept detection with several well-known classifiers using the benchmark data available from the 2007 and 2008 TRECVID projects. The results demonstrate that our WARM-based framework achieves promising performance and performs significantly better than those classifiers in the comparison.

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11

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

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

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Rahal, Imad, Dongmei Ren, and William Perrizo. "A Scalable Vertical Model for Mining Association Rules." Journal of Information & Knowledge Management 03, no. 04 (2004): 317–29. http://dx.doi.org/10.1142/s0219649204000912.

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Association rule mining (ARM) is the data-mining process for finding all association rules in datasets matching user-defined measures of interest such as support and confidence. Usually, ARM proceeds by mining all frequent itemsets — a step known to be very computationally intensive — from which rules are then derived in a straight forward manner. In general, mining all frequent itemsets prunes the space by using the downward closure (or anti-monotonicity) property of support which states that no itemset can be frequent unless all of its subsets are frequent. A large number of papers have addressed the problem of ARM but not many of them have focused on scalability over very large datasets (i.e. when datasets contain a very large number of transactions). In this paper, we propose a new model for representing data and mining frequent itemsets that is based on the P-tree technology for compression and faster logical operations over vertically structured data and on set enumeration trees for fast itemset enumeration. Experimental results presented hereinafter show big improvements for our approach over large datasets when compared to other contemporary approaches in the literature.

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13

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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14

Derouiche, Abir, Abdesslem Layeb, and Zineb Habbas. "Metaheuristics Guided by the Apriori Principle for Association Rule Mining." International Journal of Organizational and Collective Intelligence 10, no. 3 (2020): 14–37. http://dx.doi.org/10.4018/ijoci.2020070102.

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Association rule mining (ARM), one of the most known tasks in data mining, is considered as an optimization problem. The ARM problem can be solved either by exact methods or by metaheuristics. Exact methods such as Apriori algorithm are very efficient to deal with small and medium datasets. However, when dealing with large size datasets, these methods suffer from time complexity. Metaheuristics are proven faster but most of them suffer from accuracy. To deal with these two challenging issues, this work investigates to enhance metaheuristics and proposes hybrid approaches, which combine metaheuristics and the Apriori principle to intelligently explore the association rules space. To validate the proposed approaches the chemical reaction optimization metaheuristic (CRO) was used. Intensive experiments have been carried out and the first results are very promising in terms of accuracy and processing time.

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Vyas, Ranjana, Lokesh Kumar Sharma, and U. S. Tiwary. "Exploring Spatial ARM (Spatial Association Rule Mining) for Geo-Decision Support System." Journal of Computer Science 3, no. 11 (2007): 882–86. http://dx.doi.org/10.3844/jcssp.2007.882.886.

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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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Kou, Zhicong, and Lifeng Xi. "Binary Particle Swarm Optimization-Based Association Rule Mining for Discovering Relationships between Machine Capabilities and Product Features." Mathematical Problems in Engineering 2018 (October 31, 2018): 1–16. http://dx.doi.org/10.1155/2018/2456010.

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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 an efficient and cost-effective product development and production. This paper proposes a new binary particle swarm optimization- (BPSO-) based association rule mining (BPSO-ARM) method for discovering the hidden relationships between machine capabilities and product features. In particular, BPSO-ARM does not need to predefine thresholds of minimum support and confidence, which improves its applicability in real-world industrial cases. Moreover, a novel overlapping measure indication is further proposed to eliminate those lower quality rules to further improve the applicability of BPSO-ARM. The effectiveness of BPSO-ARM is demonstrated on a benchmark case and an industrial case about the automotive part manufacturing. The performance comparison indicates that BPSO-ARM outperforms other regular methods (e.g., Apriori) for ARM. The experimental results indicate that BPSO-ARM is capable of discovering important association rules between machine 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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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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Teegavarapu, Ramesh S. V. "Estimation of missing precipitation records integrating surface interpolation techniques and spatio-temporal association rules." Journal of Hydroinformatics 11, no. 2 (2009): 133–46. http://dx.doi.org/10.2166/hydro.2009.009.

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Deterministic and stochastic weighting methods are the most frequently used methods for estimating missing rainfall values. These methods may not always provide accurate estimates due to their inability to completely characterize the spatial and temporal variability of rainfall. A new association rule mining (ARM) based spatial interpolation approach is proposed, developed and investigated in the current study to estimate missing precipitation values at a gauging station. As an integrated approach this methodology combines the power of data mining techniques and spatial interpolation approaches. Data mining concepts are used to extract and formulate rules based on spatial and temporal associations among observed precipitation data series. The rules are then used to improve the precipitation estimates obtained from spatial interpolation methods. A stochastic spatial interpolation technique and three deterministic weighting methods are used as interpolation methods in the current study. Historical daily precipitation data obtained from 15 rain gauging stations from a temperate climatic region (Kentucky, USA) are used to test this approach and derive conclusions about its efficacy for estimating missing precipitation data. Results suggest that the use of association rule mining in conjunction with a spatial interpolation technique can improve the precipitation estimates.

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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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Lin, Lin, Mei-Ling Shyu, and Shu-Ching Chen. "Rule-Based Semantic Concept Classification from Large-Scale Video Collections." International Journal of Multimedia Data Engineering and Management 4, no. 1 (2013): 46–67. http://dx.doi.org/10.4018/jmdem.2013010103.

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The explosive growth and increasing complexity of the multimedia data have created a high demand of multimedia services and applications in various areas so that people can access and distribute the data easily. Unfortunately, traditional keyword-based information retrieval is no longer suitable. Instead, multimedia data mining and content-based multimedia information retrieval have become the key technologies in modern societies. Among many data mining techniques, association rule mining (ARM) is considered one of the most popular approaches to extract useful information from multimedia data in terms of relationships between variables. In this paper, a novel rule-based semantic concept classification framework using weighted association rule mining (WARM), capturing the significance degrees of the feature-value pairs to improve the applicability of ARM, is proposed to deal with major issues and challenges in large-scale video semantic concept classification. Unlike traditional ARM that the rules are generated by frequency count and the items existing in one rule are equally important, our proposed WARM algorithm utilizes multiple correspondence analysis (MCA) to explore the relationships among features and concepts and to signify different contributions of the features in rule generation. To the authors best knowledge, this is one of the first WARM-based classifiers in the field of multimedia concept retrieval. The experimental results on the benchmark TRECVID data demonstrate that the proposed framework is able to handle large-scale and imbalanced video data with promising classification and retrieval performance.

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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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Aqra, Iyad, Norjihan Abdul Ghani, Carsten Maple, José Machado, and Nader Sohrabi Safa. "Incremental Algorithm for Association Rule Mining under Dynamic Threshold." Applied Sciences 9, no. 24 (2019): 5398. http://dx.doi.org/10.3390/app9245398.

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Data mining is essentially applied to discover new knowledge from a database through an iterative process. The mining process may be time consuming for massive datasets. A widely used method related to knowledge discovery domain refers to association rule mining (ARM) approach, despite its shortcomings in mining large databases. As such, several approaches have been prescribed to unravel knowledge. Most of the proposed algorithms addressed data incremental issues, especially when a hefty amount of data are added to the database after the latest mining process. Three basic manipulation operations performed in a database include add, delete, and update. Any method devised in light of data incremental issues is bound to embed these three operations. The changing threshold is a long-standing problem within the data mining field. Since decision making refers to an active process, the threshold is indeed changeable. Accordingly, the present study proposes an algorithm that resolves the issue of rescanning a database that had been mined previously and allows retrieval of knowledge that satisfies several thresholds without the need to learn the process from scratch. The proposed approach displayed high accuracy in experimentation, as well as reduction in processing time by almost two-thirds of the original mining execution time.

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Liu, Nai Li, and Lei Ma. "Optimized Algorithm for Mining Valid and Non-Redundant Rules." Advanced Materials Research 756-759 (September 2013): 3717–22. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.3717.

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The traditional algorithm of mining association rules, or slowly produces association rules, or produces too many redundant rules, or it is probable to find an association rule, which posses high support and confidence, but is uninteresting, and even is false. Furthermore, a rule with negative-item cant be produced. This paper puts forward a new algorithm MVNR(Mining Valid and non-Redundant Association Rules Algorithm),which primely solves above problems by using the minimal subset of frequent itemset.

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

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

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Son, Le Hoang, Francisco Chiclana, Raghavendra Kumar, et al. "ARM–AMO: An efficient association rule mining algorithm based on animal migration optimization." Knowledge-Based Systems 154 (August 2018): 68–80. http://dx.doi.org/10.1016/j.knosys.2018.04.038.

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Kushwaha, Nidhi, and O. P. Vyas. "Leveraging Bibliographic RDF Data for Keyword Prediction with Association Rule Mining (ARM)^|^sup1;." Data Science Journal 13 (2014): 119–26. http://dx.doi.org/10.2481/dsj.14-033.

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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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Jiang, He, Ze Bai, Guo Ling Liu, and Xiu Mei Luan. "An Algorithm for Mining Multidimensional Positive and Negative Association Rules." Advanced Materials Research 171-172 (December 2010): 445–49. http://dx.doi.org/10.4028/www.scientific.net/amr.171-172.445.

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Research on negative association rule in multidimensional data mining is few. In this paper, an algorithm MPNAR is put forward to mine positive and negative association rules in multidimensional data. With the help of the basis of the minimum support and minimum confidence, this algorithm divided the multidimensional datasets into infrequent itemsets and frequent itemsets. The negative association rules could be mined from infrequent itemsets. Relative to the single positive association rule mining, the new additional negative association rules need not repeatedly read database because two types of association rules were simultaneously mined. Experiments show that the algorithm method is effective and valuable.

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Cen, Jun Jie, Guo Hong Gao, and Ying Jun Wang. "An Efficient Genetic Simulated Annealing Association Rules Method." Applied Mechanics and Materials 34-35 (October 2010): 927–31. http://dx.doi.org/10.4028/www.scientific.net/amm.34-35.927.

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Association rule is one of the important models of Web mining. By analyzing the topology of web site, this paper brings forward an efficient genetic simulated annealing association rules method．It applies genetic algorithm，incremental mining technology to trace users access behavior and optimizes association rules，and forecast capable association rules which improves its precision．Finally, this paper gives out the data analysis of experiment and summarizes the characteristics of genetic mining．

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

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

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Wang, Ping Shui. "A New Algorithm of Association Rules Mining Based on Relation Matrix." Advanced Materials Research 179-180 (January 2011): 55–59. http://dx.doi.org/10.4028/www.scientific.net/amr.179-180.55.

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Association rule mining is one of the hottest research areas that investigate the automatic extraction of previously unknown patterns or rules from large amounts of data. Finding association rules can be derived based on mining large frequent candidate sets. Aiming at the poor efficiency of the classical Apriori algorithm which frequently scans the business database, studying the existing association rules mining algorithms, we proposed a new algorithm of association rules mining based on relation matrix. Theoretical analysis and experimental results show that the proposed algorithm is efficient and practical.

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Lin, Zi Zhi, Si Hui Shu, and Yun Ding. "Algorithm of Mining Association Rule Based on Matrix." Applied Mechanics and Materials 513-517 (February 2014): 786–91. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.786.

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Association rule mining is one of the most important techniques of data mining. Algorithms based on matrix are efficient due to only scanning the transaction database for one time. In this paper, an algorithm of association rule mining based on the compression matrix is given. It mainly compresses the transaction matrix by integrating various strategies and fleetly finds frequent itemsets. The new algorithm optimizes the known algorithms of mining association rule based on matrix given by some researchers in recent years, which greatly reduces the temporal and spatial complexity, and highly promotes the efficiency of finding frequent itemsets.

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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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Man, Mustafa Bin, Wan Aezwani Wan Abu Bakar, Zailani Abdullah, Masita@Masila Abd Jalil, and Tutut Herawan. "Mining Association Rules: A Case Study on Benchmark Dense Data." Indonesian Journal of Electrical Engineering and Computer Science 3, no. 3 (2016): 546. http://dx.doi.org/10.11591/ijeecs.v3.i3.pp546-553.

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<p class="Abstract">Data mining is the process of discovering knowledge and previously unknown pattern from large amount of data. The association rule mining (ARM) has been in trend where a new pattern analysis can be discovered to project for an important prediction about any issues. Since the first introduction of frequent itemset mining, it has received a major attention among researchers and various efficient and sophisticated algorithms have been proposed to do frequent itemset mining. Among the best-known algorithms are Apriori and FP-Growth. In this paper, we explore these algorithms and comparing their results in generating association rules based on benchmark dense datasets. The datasets are taken from frequent itemset mining data repository. The two algorithms are implemented in Rapid Miner 5.3.007 and the performance results are shown as comparison. FP-Growth is found to be better algorithm when encountering the support-confidence framework.</p>

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Wang, Hui. "Hiding Sensitive Association Rules by Adjusting Support." Advanced Materials Research 756-759 (September 2013): 1875–78. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1875.

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Data mining technologies are successfully applied in lots of domains such as business, science research, health care, bioinformatics, financial forecasting and so on and so forth. Knowledge can be discovered by data mining and can help people to make better decisions and benefits. Association rule is one kind of the most popular knowledge discovered by data mining. While at the same time, some association rules extracted from data mining can be considered so sensitive for data holders that they will not like to share and really want to hide. Such kind of side effects of data mining is analyzed by privacy preserving technologies. In this work, we have proposed strategies by adjusting supports and quality measurements of sensitive association rules hiding.

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Shu, Si Hui, and Zi Zhi Lin. "Algorithms of Mining Maximum Frequent Itemsets Based on Compression Matrix." Applied Mechanics and Materials 571-572 (June 2014): 57–62. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.57.

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Association rule mining is one of the most important and well researched techniques of data mining, the key procedure of the association rule mining is to find frequent itemsets , the frequent itemsets are easily obtained by maximum frequent itemsets. so finding maximum frequent itemsets is one of the most important strategies of association data mining. Algorithms of mining maximum frequent itemsets based on compression matrix are introduced in this paper. It mainly obtains all maximum frequent itemsets by simply removing a set of rows and columns of transaction matrix, which is easily programmed recursive algorithm. The new algorithm optimizes the known association rule mining algorithms based on matrix given by some researchers in recent years, which greatly reduces the temporal complexity and spatial complexity, and highly promotes the efficiency of association rule mining.

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Wang, Guang Jiang, and Shi Guo Jin. "Design and Development of Intelligent Logistics System Based on Data Mining and Association Rules Technology." Advanced Materials Research 1078 (December 2014): 392–96. http://dx.doi.org/10.4028/www.scientific.net/amr.1078.392.

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Intelligent logistics is the use of integrated intelligent technology, which makes the logistics system to mimic human intelligence with the thought, perception, learning, inference and solve some problems of logistics in their ability. Association rule mining is usually more applicable and recorded in the index of discrete values. This paper analyzes theory and algorithm research of association rules data mining and presents design and development of intelligent logistics system based on data mining and association rules technology.

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Asha, P., T. Prem Jacob, and A. Pravin. "Finding Efficient Positive and Negative Itemsets Using Interestingness Measures." International Journal of Engineering & Technology 7, no. 4.36 (2018): 533. http://dx.doi.org/10.14419/ijet.v7i4.36.24133.

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Currently, data gathering techniques have increased through which unstructured data creeps in, along with well defined data formats. Mining these data and bringing out useful patterns seems difficult. Various data mining algorithms were put forth for this purpose. The associated patterns generated by the association rule mining algorithms are large in number. Every ARM focuses on positive rule mining and very few literature has focussed on rare_itemsets_mining. The work aims at retrieving the rare itemsets that are of most interest to the user by utilizing various interestingness measures. Both positive and negative itemset mining would be focused in this work.

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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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Zhai, Yue, Xi Yu, and An Sheng Deng. "Class Association Rule Mining Based on Incremental Construction of Lattice." Applied Mechanics and Materials 571-572 (June 2014): 345–50. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.345.

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In the problem of mining classification rules, previous methods are too general or too over-fitting for a given dataset. In this paper, through analyzing the characteristics of concept lattice and indiscernible matrix, classification rule acquisition based on lattice was proposed. Our method includes two phase: (1) proposing incremental generating the nodes of lattice using indiscernible matrix. (2) Developing some theorems for pruning redundant rules quickly. It is shown by experimental results that our approach not only results in shorter execution times, but also avoids missing important rules than the generalization of previous known methods.

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Senthil, D., and G. Suseendran. "Efficient time series data classification using sliding window technique based improved association rule mining with enhanced support vector machine." International Journal of Engineering & Technology 7, no. 3.3 (2018): 218. http://dx.doi.org/10.14419/ijet.v7i2.33.13890.

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Time series analysis is an important and complex problem in machine learning and statistics. In the existing system, Support Vector Machine (SVM) and Association Rule Mining (ARM) is introduced to implement the time series data. However it has issues with lower accuracy and higher time complexity. Also it has issue with optimal rules discovery and segmentation on time series data. To avoid the above mentioned issues, in the proposed research Sliding Window Technique based Improved ARM with Enhanced SVM (SWT-IARM with ESVM) is proposed. In the proposed system, the preprocessing is performed using Modified K-Means Clustering (MKMC). The indexing process is done by using R-tree which is used to provide faster results. Segmentation is performed by using SWT and it reduces the cost complexity by optimal segments. Then IARM is applied on efficient rule discovery process by generating the most frequent rules. By using ESVM classification approach, the rules are classified more accurately.

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Li, Jian Hong. "Association Rule-Based Novel Incremental Updating Algorithm." Advanced Materials Research 317-319 (August 2011): 1868–71. http://dx.doi.org/10.4028/www.scientific.net/amr.317-319.1868.

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This paper focuses on an important research topic in data mining (DM) which heavily replies on the association rules. In order to deal with the maintenance issues within the background of the static transaction database, there are some minor changes to minimum support and confidence coefficient. A novel algorithm based on incremental updated is proposed, which is termed as NIUA (Novel Incremental Updating Algorithm). IUA uses association rules to mining the database, aiming at finding the potential information or finding the reasons from massive data.

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Cremaschi, Paolo, Roberta Carriero, Stefania Astrologo, et al. "An Association Rule Mining Approach to Discover lncRNAs Expression Patterns in Cancer Datasets." BioMed Research International 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/146250.

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In the past few years, the role of long noncoding RNAs (lncRNAs) in tumor development and progression has been disclosed although their mechanisms of action remain to be elucidated. An important contribution to the comprehension of lncRNAs biology in cancer could be obtained through the integrated analysis of multiple expression datasets. However, the growing availability of public datasets requires new data mining techniques to integrate and describe relationship among data. In this perspective, we explored the powerness of the Association Rule Mining (ARM) approach in gene expression data analysis. By the ARM method, we performed a meta-analysis of cancer-related microarray data which allowed us to identify and characterize a set of ten lncRNAs simultaneously altered in different brain tumor datasets. The expression profiles of the ten lncRNAs appeared to be sufficient to distinguish between cancer and normal tissues. A further characterization of this lncRNAs signature through a comodulation expression analysis suggested that biological processes specific of the nervous system could be compromised.

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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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H.N, Neema, and Balaji Dr. N.V. "Study of Attention-Deficit Hyperactivity Disorder (ADHD) in Children- Applying Association Rule Mining (ARM) (Survey)." IJARCCE 8, no. 6 (2019): 76–79. http://dx.doi.org/10.17148/ijarcce.2019.8616.

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Xu, Hong Sheng, Qing Tan, and Chao Li. "Study on Building Wall Materials System by Using Association Rule Mining." Applied Mechanics and Materials 327 (June 2013): 193–96. http://dx.doi.org/10.4028/www.scientific.net/amm.327.193.

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Association rule mining is to find interesting associations between itemsets in large amounts of data or related links. The new wall materials are mainly concrete, cement or fly ash, coal gangue and other industrial waste and household garbage produced by the non-clay brick, building blocks and building boards and construction techniques, materials, technology, detection means there is no specification limit. This paper presents the using association rule mining to build the building wall materials system. Experimental data sets prove that the proposed algorithm is effective and reasonable.

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Gong, Song Jie. "Personalized Recommendation System Based on Association Rules Mining and Collaborative Filtering." Applied Mechanics and Materials 39 (November 2010): 540–44. http://dx.doi.org/10.4028/www.scientific.net/amm.39.540.

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With the rapidly growing amount of information available, the problem of information overload is always growing acute. Personalized recommendations are an effective way to get user recommendations for unseen elements within the enormous volume of information based on their preferences. The personalized recommendation system commonly used methods are content-based filtering, collaborative filtering and association rule mining. Unfortunately, each method has its drawbacks. This paper presented a personalized recommendation method combining the association rules mining and collaborative filtering. It used the association rules mining to fill the vacant where necessary. And then, the presented approach utilizes the user based collaborative filtering to produce the recommendations. The recommendation method combining association rules mining and collaborative filtering can alleviate the data sparsity problem in the recommender systems.

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