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An Improved Algorithm for Mining Correlation Item Pairs

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Computers, Materials & Continua 2020年第10期65卷 337-354页

作者： Tao Li Yongzhen Ren Yongjun Ren Jinyue Xia College of Electronic and Information Engineering Nanjing University of Information Science and TechnologyNanjing210044China College of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China International Business Machines Corporation(IBM) New YorkUSA

Apriori algorithm is often used in traditional association rules mining,searching for the mode of higher *** the correlation rules are obtained by detected the correlation of the item sets,but this tends to ignore low-support high-correlation of association *** view of the above problems,some scholars put forward the positive correlation coefficient based on Phi correlation to avoid the embarrassment caused by Apriori *** can dig item sets with low-support but *** the algorithm has pruned the search space,it is not obvious that the performance of the running time based on the big data set is reduced,and the correlation pairs can be *** paper presents an improved mining algorithm with new association rules based on interestingness for correlation pairs,using an upper bound on interestingness of the supersets to prune the search *** greatly reduces the running time,and filters the meaningless correlation pairs according to the constraints of the *** with the algorithm based on the Phi correlation coefficient,the new algorithm has been significantly improved in reducing the running time,the result has pruned the redundant correlation *** it improves the mining efficiency and accuracy.

关键词： interestingness item pairs positive correlation association rules redundancy

CARM:Context Based Association Rule Mining for Conventional Data

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Computers, Materials & Continua 2021年第9期68卷 3305-3322页

作者： Muhammad Shaheen Umair Abdullah Faculty of Engineering&Information Technology Foundation UniversityIslamabadPakistan Department of Computer Science Barani Institute of Information TechnologyRawalpindiPakistan

This paper is aimed to develop an algorithm for extracting association rules,called Context-Based Association Rule Mining algorithm(CARM),which can be regarded as an extension of the Context-Based Positive and Negative Association Rule Mining algorithm(CBPNARM).CBPNARM was developed to extract positive and negative association rules from Spatiotemporal(space-time)data only,while the proposed algorithm can be applied to both spatial and non-spatial *** proposed algorithm is applied to the energy dataset to classify a country’s energy development by uncovering the enthralling interdependencies between the set of variables to get positive and negative *** association rules related to sustainable energy development are extracted by the proposed algorithm that needs to be pruned by some pruning *** context,in this paper serves as a pruning measure to extract pertinent association rules from non-spatial *** Probability Increment Ratio(CPIR)is also added in the proposed algorithm that was not used in *** inclusion of the context variable and CPIR resulted in fewer rules and improved robustness and ease of ***,the extraction of a common negative frequent itemset in CARM is different from that of *** rules created by the proposed algorithm are more meaningful,significant,relevant and *** accuracy of the proposed algorithm is compared with the Apriori,PNARM and CBPNARM *** results demonstrated enhanced accuracy,relevance and timeliness.

关键词： Association rules context CBPNARM non-spatial data CPIR support confidence interestingness

An Effective Network Traffic Data Control Using Improved Apriori Rule Mining

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Circuits and Systems 2016年第10期7卷 3162-3173页

作者： Subbiyan Prakash Murugasamy Vijayakumar Department of Information Technology Sri Shakthi Institute of Engineering and Technology Coimbatore India Department of Computer Science and Engineering Nandha College of Technology Erode India

The increasing usage of internet requires a significant system for effective communication. To pro- vide an effective communication for the internet users, based on nature of their queries, shortest routing path is usually preferred for data forwarding. But when more number of data chooses the same path, in that case, bottleneck occurs in the traffic this leads to data loss or provides irrelevant data to the users. In this paper, a Rule Based System using Improved Apriori (RBS-IA) rule mining framework is proposed for effective monitoring of traffic occurrence over the network and control the network traffic. RBS-IA framework integrates both the traffic control and decision making system to enhance the usage of internet trendier. At first, the network traffic data are ana- lyzed and the incoming and outgoing data information is processed using apriori rule mining algorithm. After generating the set of rules, the network traffic condition is analyzed. Based on the traffic conditions, the decision rule framework is introduced which derives and assigns the set of suitable rules to the appropriate states of the network. The decision rule framework improves the effectiveness of network traffic control by updating the traffic condition states for identifying the relevant route path for packet data transmission. Experimental evaluation is conducted by extrac- ting the Dodgers loop sensor data set from UCI repository to detect the effectiveness of theproposed Rule Based System using Improved Apriori (RBS-IA) rule mining framework. Performance evaluation shows that the proposed RBS-IA rule mining framework provides significant improvement in managing the network traffic control scheme. RBS-IA rule mining framework is evaluated over the factors such as accuracy of the decision being obtained, interestingness measure and execution time.

关键词： Network Traffic Internet Traffic Condition Rule Mining Decision Rule Framework interestingness Traffic Data Web Log

Extracting Leading Joint Causes of Death and Mining Associations between Them

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Open Journal of Epidemiology 2016年第1期6卷 28-43页

作者： Keamogetse Setlhare Ntonghanwah Forcheh Department of Statistics University of Botswana Gaborone Botswana

The gains in analyzing death from a multiple cause perspective have been recognized for a very long time. Methods that have been adopted have sought to determine additional gains made by treating death as a multiple cause phenomenon as compared to analysis based on a single under-lying cause. This paper shows how association rules mining methodology can be adapted to determine joint morbid causes with strong and interesting associations. Results show that some causes of death that do not appear among the leading causes show strong associations with other causes that would otherwise remain unknown without the use of association rules methodology. Overall, the study found that the leading joint pair of causes of death in South Africa was metabolic disorders and intestinal infectious diseases which accounted for 18.9 deaths per 1000 in 2008, followed by cerebrovascular and hypertensive diseases which accounted for 18.3 deaths per 1000.

关键词： Association Rule Confidence interestingness Multiple Cause of Death South Africa Odds-Ratio Prevalence Support

ARMiner:一个基于关联规则的数据采掘工具

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ARMiner:一个基于关联规则的数据采掘工具

一个基于兴趣度包含负属性项的关联规则采掘算法

第十八届全国数据库学术会议

作者：周皓峰阮备军朱建秋朱扬勇施伯乐复旦大学计算机科学系

1引言数据采掘技术所表现出的广阔前景吸引了国内外众多研究人员和商业机构,一大批新的研究结果和应用系统或原型被开发了出来。其中有些系统在各个领域都取得了成功,如IBM的Intelligent Miner和Simon Fraser大学的DBMiner,南京大学的Kn... 详细信息

1引言数据采掘技术所表现出的广阔前景吸引了国内外众多研究人员和商业机构,一大批新的研究结果和应用系统或原型被开发了出来。其中有些系统在各个领域都取得了成功,如IBM的Intelligent Miner和Simon Fraser大学的DBMiner,南京大学的Knight等,这些系统代表了当前数据采掘技术的发展。

关键词： association rules negative item interestingness concept hierarchy

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一个基于兴趣度包含负属性项的关联规则采掘算法