A novel association rule mining approach using TID intermediate itemset

Abstract

Designing an efficient association rule mining (ARM) algorithm for multilevel knowledge-based transactional databases that is appropriate for real-world deployments is of paramount concern. However, dynamic decision making that needs to modify the threshold either to minimize or maximize the output knowledge certainly necessitates the extant state-of-the-art algorithms to rescan the entire database. Subsequently, the process incurs heavy computation cost and is not feasible for real-time applications. The paper addresses efficiently the problem of threshold dynamic updation for a given purpose. The paper contributes by presenting a novel ARM approach that creates an intermediate itemset and applies a threshold to extract categorical frequent itemsets with diverse threshold values. Thus, improving the overall efficiency as we no longer needs to scan the whole database. After the entire itemset is built, we are able to obtain real support without the need of rebuilding the itemset (e.g. Itemset list is intersected to obtain the actual support). Moreover, the algorithm supports to extract many frequent itemsets according to a pre-determined minimum support with an independent purpose. Additionally, the experimental results of our proposed approach demonstrate the capability to be deployed in any mining system in a fully parallel mode; consequently, increasing the efficiency of the real-time association rules discovery process. The proposed approach outperforms the extant state-of-the-art and shows promising results that reduce computation cost, increase accuracy, and produce all possible itemsets.

The influence of machine learning-based knowledge management model on enterprise organizational capability innovation and industrial development

“…For the multilayer association mining method, according to the actual situation, it can be divided into two major types. One is to use the same support level in all component layers, that is, to use a consistent minimum support threshold in each component layer, which simplifies the data collection process, but the setting of thresholds in this type is a challenging task; another is the use of successively lower levels of support in the lower levels; in this type, a variety of data collection methods can be used, such as layer-by-layer independent methods and single-item filtering methods; such setting makes this type more sensitive in the mining of multilayer data, and can reduce the generation of invalid associations [ 28 , 29 ].…”

A Theoretical Approach for Correlating Proteins to Malignant Diseases

“…There is no application for their method. Aqra et al (2018) presented an Intermediate Transaction ID Apriori (ITD Apriori) algorithm where a new itemset format structure is adopted to address the problem of threshold that necessitates rescanning the entire database. This approach creates an intermediate itemset.…”

“…Many researchers have done modifications to improve the efficiency of the Apriori algorithm and to overcome some of the limitations of the Apriori algorithm. They used some methods to improve Apriori efficiency as Intersection (Aqra et al, 2018), Hash-based itemset counting (Park et al, 1995;Vyas and Sherasiya, 2016), Partitioning (Jia et al, 2012), Sampling (Toivonen, 1996;Rajeswari, 2015), etc. The Intersection is a method used to improve memory management, efficiently, by reducing the computation cost of Apriori and removing its complexity.…”

“…Moreover, the support is calculated by counting the common transactions that contain each element of the candidate set. This takes less time than the original algorithm; however, data must be in a vertical layout (Agrawal et al, 2013;Raval et al, 2013;Aqra et al, 2018).…”

Efficient Association Rules Hiding Using Genetic Algorithms

“…Association rule mining is the most extensively used (and promising) technique in the data mining field [14]. Since its introduction, it has created several opportunities to mine high-utility data [2].…”