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An Improvement of FP-Growth Mining Algorithm Using Linked list
http://hdl.handle.net/20.500.12678/0000004599
http://hdl.handle.net/20.500.12678/0000004599595a1412-facb-4575-8100-6cde008f75de
99bbbac9-c7b0-42b4-9a64-94f8b8dc4173
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Title | ||||||
Title | An Improvement of FP-Growth Mining Algorithm Using Linked list | |||||
Language | en | |||||
Publication date | 2020-02-28 | |||||
Authors | ||||||
Maw, San San | ||||||
Description | ||||||
Frequent pattern mining such as association rules,clustering, and classification is one of the most centralareas in the data mining research. One of the foremostprocesses in association rule mining is the discovering ofthe frequent pattern. To draw on all substantial frequentpatterns from the sizable amount of transaction data,various algorithms have been proposed. The proposedresearch aims to mine frequent patterns from the sizableamount of transaction database by using linked list. In thismethod, first scanning the database, the count of frequent 1-itemsets is searched using the hash map and for nextitemsets, it is stored in the linked list, second scanning thedatabase. The frequent 2-itemsets is generated using hashtable and so on. So, the proposed research needs only twoscans and this proposed method requires shorter processingtime and smaller memory space. | ||||||
Keywords | ||||||
frequent pattern mining, data mining, linked list,, hash table | ||||||
Identifier | 978-1-7281-5925-6 | |||||
Journal articles | ||||||
Proceedings of the Eighteenth International Conference On Computer Applications (ICCA 2020) | ||||||
Conference papers | ||||||
Books/reports/chapters | ||||||
Thesis/dissertations |