CARM: Context Based Association Rule Mining for Conventional Data

Abstract

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 data. The 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 associations. Many association rules related to sustainable energy development are extracted by the proposed algorithm that needs to be pruned by some pruning technique. The context, in this paper serves as a pruning measure to extract pertinent association rules from non-spatial data. Conditional Probability Increment Ratio (CPIR) is also added in the proposed algorithm that was not used in CBPNARM. The inclusion of the context variable and CPIR resulted in fewer rules and improved robustness and ease of use. Also, the extraction of a common negative frequent itemset in CARM is different from that of CBPNARM. The rules created by the proposed algorithm are more meaningful, significant, relevant and insightful. The accuracy of the proposed algorithm is compared with the Apriori, PNARM and CBPNARM algorithms. The results demonstrated enhanced accuracy, relevance and timeliness.

Mining significant local spatial association rules for multi-category point data

“…Spatial association rule mining is oriented towards spatial data which often show spatial dependence and spatial heterogeneity. According to the mining scale, spatial association rule mining methods can be divided into global spatial association rule mining and local spatial association rule mining [31] .…”

A big data association rule mining based approach for energy building behaviour analysis in an IoT environment

“…19,20 or Refs. [21][22][23] . In the former, it is reviewed how some of the traditional Data Mining techniques have been used to analyse building-related data 19 .…”

An Optimized Association Rules Mining Framework for Chinese Social Insurance Fund Data Auditing

“…Te work in reference [21] incorporated frequent itemsets with domain knowledge in the form of a taxonomy to mine negative association rules. Shaheen and Abdullah developed a series of algorithms for diferent felds, such as exploring positive and negative context-based association rules for conventional/characteristic data [24,25], and mining context-based association rules on microbial databases to extract interesting and useful associations of microbial attributes with existence of hydrocarbon reserve [26][27][28][29]. It should be noted that some contradictory rules may be mined when positive and negative rules are mined simultaneously, such as A ⇒ B and A ⇒ ¬B are both strong rules [30][31][32].…”