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Top1. Introduction
Frequent itemsets mining (FIM) is a very important branch in data mining as it discovers the hidden related frequent itemsets in datasets. But it will be very necessary and precise that if the frequent itemsets are discovered based on their importance to users instead of the frequency of itemsets existence only. This is very useful for the recommendation system application based on market datasets as each item in market has different profit, and the application based on event logs to determine the related pages based on dwelling time of the users instead of the number of visiting these pages. We may use the real-world application data collected from different sensors. This data may face any data distortion problems such as noise problem. These problems may make the data incomplete or imprecise. So, it is necessary to find a good technique to treat with the uncertainty of data to get a precise mining result.
When the importance and the uncertainty of data are taken into account in FIMA, the result of finding the most frequent itemsets for all real-world application such as healthcare applications, recommendation system applications will be determined precisely. Some of the previous algorithms of FIM do not take into their considerations two critical issues, which are: (1) the importance of each item and, (2) the uncertainty of used dataset.
Regarding the first issue, FIM algorithms treat all items without taking into account the importance of different items. All items have the same importance or weight value. In some FIM algorithms, the number of existence frequency in the database is the only measure for determining the frequent itemsets, but this is not sufficient as some itemsets will be ignored because of infrequently presence although they have more importance than the itemsets which are more frequent. There is a solution to deal with this limitation by defining weights for items according to some criteria such as user preference, profits, interestingness, or dwelling time on websites. There are some proposed algorithms to mine the weighted frequent itemsets (Cai et al., 1998; Yun & Leggett, 2005; Sun& Bai, 2008; Lan et al., 2013; Lan et al., 2014; Lin et al., 2015; Zhao et al., 2018; Chee et al., 2018). However, these algorithms don't consider the data uncertainty.
The second limitation of FIM algorithms is the neglecting of data uncertainty. The real-world data, such as data collected from practical sensor applications, may be inaccurate or imprecise. Some algorithms are proposed to find the frequent itemset from uncertain databases, which are classified into two categories of probabilistic frequent itemset mining (Bernecker et al., 2009) and expected support model (Chui et al., 2007; Sun & Bai, 2008; Aggarwal et al., 2009; Shah & Halim, 2018; Braun et al., 2018).
Recently, some algorithms tried to find out weighted frequent itemset from an uncertain database (Lin et al., 2016, Lin et al., 2016). For example, Lin et al. (2016) proposed high expected weighted itemsets-Uapriori (HEWI- UApriori) algorithm based on the UApriori algorithm. It is applied in two-phase to find the most weighted frequent itemsets. Lin et al. (2016) proposed a high expected weighted itemsets-Utree(HEWI-Utree) algorithm to decrease the multiple database scan without generating enormous candidates. The first algorithm is suffering from the multiple database scan which leads to increase the execution time and the second algorithm may consume more memory. So, we need to use algorithm which solves this problem of multiple scan of database high memory cost. Handling the two issues is very necessary for treating with the IOT applications and big data applications (Kinnunen et al., 2018; Galli, 2018; Krimpmann & Stithmeter, 2017; Mizuno & Odake, 2017)