A New Algorithm of Association Rules Mining Based on Relation Matrix

2011 ◽  
Vol 179-180 ◽  
pp. 55-59
Author(s):  
Ping Shui Wang
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Automatic Extraction ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Association Rules Mining ◽  
Research Areas ◽  
Mining Algorithms ◽  
Relation Matrix

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.

scholarly journals EARMGA and Apriori Algorithm's Performance Evaluation for Association Rule Mining

2019 ◽  
Vol 9 (1) ◽  
pp. 6982-6987
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
The Other ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Large Databases ◽  
Algorithm Comparison ◽  
Average Confidence ◽  
Mining Algorithms

Association rule mining techniques are important part of data mining to derive relationship between attributes of large databases. Association related rule mining have evolved huge interest among researchers as many challenging problems can be solved using them. Numerous algorithms have been discovered for deriving association rules effectively. It has been evaluated that not all algorithms can give similar results in all scenarios, so decoding these merits becomes important. In this paper two association rule mining algorithms were analyzed, one is popular Apriori algorithm and the other is EARMGA (Evolutionary Association Rules Mining with Genetic Algorithm). Comparison of these two algorithms were experimentally performed based on different datasets and different parameters like Number of rules generated, Average support, Average Confidence, Covered records were detailed.

A Survey on Fuzzy Association Rule Mining

2013 ◽  
Vol 9 (1) ◽  
pp. 1-27 ◽  
Author(s):  
Harihar Kalia ◽  
Satchidananda Dehuri ◽  
Ashish Ghosh
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Quantitative Data ◽  
Association Rule Mining ◽  
Real Life ◽  
Rule Mining ◽  
Fuzzy Association Rules ◽  
Fuzzy Association Rule ◽  
Mining Algorithms ◽  
Fuzzy Association Rule Mining

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.

scholarly journals Present State-of-The-ART of Dynamic Association Rule Mining Algorithms

2019 ◽  
Vol 9 (1) ◽  
pp. 309-316
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Healthcare Data ◽  
Data Mining Approach ◽  
Mining Community ◽  
Pattern Growth ◽  
Mining Algorithms

Association Rule Mining (ARM) is a data mining approach for discovering rules that reveal latent associations among persisted entity sets. ARM has many significant applications in the real world such as finding interesting incidents, analyzing stock market data and discovering hidden relationships in healthcare data to mention few. Many algorithms that are efficient to mine association rules are found in the existing literature, apriori-based and Pattern-Growth. Comprehensive understanding of them helps data mining community and its stakeholders to make expert decisions. Dynamic update of association rules that have been discovered already is very challenging due to the fact that the changes are arbitrary and heterogeneous in the kind of operations. When new instances are added to existing dataset that has been subjected to ARM, only those instances are to be used in order to go for incremental mining of rules instead of considering the whole dataset again. Recently some algorithms were developed by researchers especially to achieve incremental ARM. They are broadly grouped into Apriori-based and Pattern-Growth. This paper provides review of Apriori-based and Pattern-Growth techniques that support incremental ARM.

Association Rule Mining and Evaluation Based on Information Security Vulnerabilities Main Body

2014 ◽  
Vol 687-691 ◽  
pp. 1282-1285 ◽  
Author(s):  
Ying Sui
Keyword(s):  
Information Security ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Main Body ◽  
Apriori Algorithm ◽  
User Interest ◽  
Rule Mining ◽  
Security Vulnerabilities ◽  
Evaluation Stage

Information security is a matter of concern in any sector and industry, and the vulnerability is the important factor which caused this issue. Therefore it is necessary to analyze and predict the occurrence of vulnerability. This paper used the datas of CNNVD vulnerability database and Apriori algorithm to analyze and predict the occurrence of software vulnerability. In the data preprocessing stage by changing the level of vulnerability rule we can dig out more concept association. In the evaluation stage of association rules by designing filters we can find the rules in line with the degree of user interest. Finally, this papper could demonstrate the effectiveness of of this method by experiments.

Cross Sellingusing Association Rule Mining

2014 ◽  
Vol 687-691 ◽  
pp. 1337-1341
Author(s):  
Ran Bo Yao ◽  
An Ping Song ◽  
Xue Hai Ding ◽  
Ming Bo Li
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
New Method ◽  
Experimental Result ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Frequent Item ◽  
Direct Benefits ◽  
Frequent Item Sets

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.

Personalized Recommendation System Based on Association Rules Mining and Collaborative Filtering

2010 ◽  
Vol 39 ◽  
pp. 540-544 ◽  
Author(s):  
Song Jie Gong
Keyword(s):  
Collaborative Filtering ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Recommendation System ◽  
Information Overload ◽  
Personalized Recommendation ◽  
Rule Mining ◽  
Association Rules Mining ◽  
Content Based Filtering

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.

A Hybrid Pre-Post Constraint-Based Framework for Discovering Multi-Dimensional Association Rules Using Ontologies

2019 ◽  
Vol 14 (1) ◽  
pp. 112-131
Author(s):  
Emad Alsukhni ◽  
Ahmed AlEroud ◽  
Ahmad A. Saifan
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Sets ◽  
Rule Mining ◽  
Useful Knowledge ◽  
Novel Association ◽  
Rule Pruning ◽  
Occurrence Patterns ◽  
Mining Algorithms

Association rule mining is a very useful knowledge discovery technique to identify co-occurrence patterns in transactional data sets. In this article, the authors proposed an ontology-based framework to discover multi-dimensional association rules at different levels of a given ontology on user defined pre-processing constraints which may be identified using, 1) a hierarchy discovered in datasets; 2) the dimensions of those datasets; or 3) the features of each dimension. The proposed framework has post-processing constraints to drill down or roll up based on the rule level, making it possible to check the validity of the discovered rules in terms of support and confidence rule validity measures without re-applying association rule mining algorithms. The authors conducted several preliminary experiments to test the framework using the Titanic dataset by identifying the association rules after pre- and post-constraints are applied. The results have shown that the framework can be practically applied for rule pruning and discovering novel association rules.

Association Rule Mining Based on Multidimensional Pattern Relations

2014 ◽  
Vol 918 ◽  
pp. 243-245
Author(s):  
Yu Ke Chen ◽  
Tai Xiang Zhao
Keyword(s):  
Decision Support ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Multidimensional Data ◽  
Incremental Mining ◽  
Rule Mining ◽  
Online Mining ◽  
Mining Algorithms

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.

An Efficient Communication Network SDH Alarm Association Rule Mining Algorithm

2014 ◽  
Vol 926-930 ◽  
pp. 1870-1873
Author(s):  
Hui Sheng Gao ◽  
Ying Min Li
Keyword(s):  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Time Window ◽  
Apriori Algorithm ◽  
Rule Mining ◽  
Event Window ◽  
Mining Algorithm ◽  
Efficient Communication ◽  
Algorithm Framework

WINEPI algorithm is kind of data mining technology that is widely used in alarm association rules mining. Based on the classic WINEPI algorithm, we apply event window instead of time window to improve the exploration result, meanwhile we use FP-Growth algorithm framework instead of Apriori algorithm framework , thus improving efficiency. Based on the alarm time attribute we find interesting alarm association rules further. Experiments show that compared with the classic WINEPI algorithm our improved approach have advantages in reducing the mining error rate and gaining more interesting alarm association rules.

Research and Application of Improved Apriori Algorithm Based on Matrix

2014 ◽  
Vol 668-669 ◽  
pp. 1102-1105
Author(s):  
Yuan Liu ◽  
Yuan Sheng Lou
Keyword(s):  
Decision Support ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Colleges And Universities ◽  
Apriori Algorithm ◽  
Selection System ◽  
Course Selection ◽  
Rule Mining ◽  
Provide Decision Support

This article put forward a NCM_Apriori algorithm, which through compressing matrix and reducing the scan times to reduce the database I/O overhead, effectively improve the efficiency of association rule mining. At the same time in the process of generating association rules, computation is greatly reduced by using the nature of probability. And applies the algorithm to the mining of students' course selection system, which can provide decision support for colleges and universities.

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