CARM:Context Based Association Rule Mining for Conventional Data

作     者：Muhammad Shaheen Umair Abdullah 

作者机构：Faculty of Engineering&Information TechnologyFoundation UniversityIslamabadPakistan Department of Computer ScienceBarani Institute of Information TechnologyRawalpindiPakistan 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第68卷第9期

页      面：3305-3322页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Association rules context CBPNARM non-spatial data CPIR support confidence interestingness 

摘      要：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.