Minimum threshold determination method based on dataset characteristics in association rule mining

Abstract

Association rule mining is a technique that is widely used in data mining. This technique is used to identify interesting relationships between sets of items in a dataset and predict associative behavior for new data. Before the rule is formed, it must be determined in advance which items will be involved or called the frequent itemset. In this step, a threshold is used to eliminate items excluded in the frequent itemset which is also known as the minimum support. Furthermore, the threshold provides an important role in determining the number of rules generated. However, setting the wrong threshold leads to the failure of the association rule mining to obtain rules. Currently, user determines the minimum support value randomly. This leads to a challenge that becomes worse for a user that is ignorant of the dataset characteristics. It causes a lot of memory and time consumption. This is because the rule formation process is repeated until it finds the desired number of rules. The value of minimum support in the adaptive support model is determined based on the average and total number of items in each transaction, as well as their support values. Furthermore, the proposed method also uses certain criteria as thresholds, therefore, the resulting rules are in accordance with user needs. The minimum support value in the proposed method is obtained from the average utility value divided by the total existing transactions. Experiments were carried out on 8 specific datasets to determine the association rules using different dataset characteristics. The trial of the proposed adaptive support method uses 2 basic algorithms in the association rule, namely Apriori and Fpgrowth. The test is carried out repeatedly to determine the highest and lowest minimum support values. The result showed that 6 out of 8 datasets produced minimum and maximum support values for the apriori and fpgrowth algorithms. This means that the value of the proposed adaptive support has the ability to generate a rule when viewed from the quality as adaptive support produces at a lift ratio value of > 1. The dataset characteristics obtained from the experimental results can be used as a factor to determine the minimum threshold value.

Rule-ranking method based on item utility in adaptive rule model

“…Figure 1 shows that the model has several functions which have been discussed in previous studies ( Hikmawati, Maulidevi & Surendro, 2020 , 2021a , 2021b ; Hikmawati & Surendro, 2020 ) and the three main functions are explained as follows: 1. Mining of association rules from different databases in terms of sources, characteristics, and attributes.…”

“…Automatic threshold value determination process. The system can automatically calculate the threshold value according to the characteristics of the database and the criteria desired by the users and this means there is no need to determine the minimum support and minimum confidence values at the beginning ( Hikmawati, Maulidevi & Surendro, 2021b ). 3.…”

“…The dataset used for this experiment, as previously described in Erna ( Hikmawati, Maulidevi & Surendro, 2021b ), is a special dataset for the rule association case. This dataset is obtained from SPMF ( Fournier-Viger et al, 2016 ), UCI Dataset ( Casey & Dua, 2019 ) and real transaction data.…”

“…The adaptive rule method proposed by Hikmawati, Maulidevi & Surendro (2020 , 2021a , 2021b) and Hikmawati & Surendro (2020) has the ability to determine the frequent itemsets based on the occurrence frequency of an item and another criterion called item utility ( Krishnamoorthy, 2018 ; Lin et al, 2019 ; Liu & Qu, 2012 ; Nguyen et al, 2019 ) to produce adaptive rule according to the criteria desired by the user. There is also a function in this model usually used to determine the minimum value of support based on the characteristics of the dataset as well as other criteria added to serve as an assessment tool in the rule formation process which is called adaptive support ( Hikmawati, Maulidevi & Surendro, 2021b ).…”

Pruning Strategy on Adaptive Rule Model by Sorting Utility Items

“…Therefore, the association rule needs to be adaptive according to users' needs, both in terms of the criteria involved in the formation process and threshold value. This is owned by the adaptive rule model [30]- [33]. Adaptive rule is a method of developing association rules that can produce rules according to the wishes of users and the characteristics of the data used.…”