Browsing by Subject "apriori algorithm"

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Access status: Open Access ,
Finding frequent items: novel method for improving Apriori algorithm
(Wydawnictwa AGH, 2022) Karimtabar, Noorollah; Fard, Mohammad Javad Shayegan
In this paper, we use an intelligent method for improving the Apriori algorithm in order to extract frequent itemsets. PAA (the proposed Apriori algorithm) pursues two goals: first, it is not necessary to take only one data item at each step – in fact, all possible combinations of items can be generated at each step, and second, we can scan only some transactions instead of scanning all of the transactions to obtain a frequent itemset. For performance evaluation, we conducted three experiments with the traditional Apriori, BitTableFI, TDM-MFI, and MDC-Apriori algorithms. The results exhibited that the algorithm execution time was significantly reduced due to the significant reduction in the number of transaction scans to obtain the itemset. As in the first experiment, the time that was spent to generate frequent items underwent a reduction of 52% as compared to the algorithm in the first experiment. In the second experiment, the amount of time that was spent was equal to 65%, while in the third experiment, it was equal to 46%.
Access status: Open Access ,
Set representation for rule-generation algorithms
(Wydawnictwa AGH, 2022) Kharkongor, Carynthia; Nath, Bhabesh
The task of mining association rules has become one of the most widely used discovery pattern methods in knowledge discovery in databases (KDD). One such task is to represent an item set in the memory. The representation of the item set largely depends on the type of data structure that is used for storing them. Computing the process of mining an association rule impacts the memory and time requirements of the item set. With the constant increase of the dimensionality of data and data sets, mining such a large volume of data sets will be difficult since all of these item sets cannot be placed in the main memory. As the representation of an item set greatly affects the efficiency of the rule-mining association, a compact and compressed representation of the item set is needed. In this paper, a set representation is introduced that is more memory- and cost-efficient. Bitmap representation takes 1 byte for an element, but a set representation uses 1 bit. The set representation is being incorporated in the Apriori algorithm. Set representation is also being tested for different rule-generation algorithms. The complexities of these different rule-generation algorithms that use set representation are being compared in terms of memory and time of execution.