Weighted Frequent Itemset Mining Using Weighted Subtrees: WST-WFIM

作     者：Nalousi, Saeed Farhang, Yousef Sangar, Amin Babazadeh 

作者机构：Islamic Azad Univ Urmia Branch Dept IT & Comp Engn Orumiyeh *** Iran Islamic Azad Univ Khoy Branch Dept IT & Comp Engn Khoy Iran 

出 版 物：《IEEE CANADIAN JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING》 (iEEE Can J Electr Comput Eng)

年 卷 期：2021年第44卷第2期

页      面：206-215页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Performance evaluation Drugs Runtime Itemsets Maintenance engineering Approximation algorithms Data structures Data mining shared transactions weighted items weighted rule generation 

摘      要：There is a wide range of association rules mining algorithms concerning the importance of weighted data to extract frequent rules. In some algorithms, each item has only one weight in the data set, while, in others, each item in each segment of the data set has only one specific weight. In this work, a new efficient algorithm named weighted frequent itemset mining using weighted subtrees (WST-WFIM) has been proposed to discover the average weight of frequent rules. This algorithm uses specific trees and some new data structures on the frequent pattern growth (FP-Growth) algorithm to calculate the average weight of discovered rules. It works on the data set that each item in each transaction has a specific weight and keeps them in the dedicated tree. Moreover, we have proposed the shared transactions concept and WST-WFIM by using it and calculate the average weight for discovered frequent rules. The algorithm capability was evaluated by standard sparse and dense weighted data sets. The results show that, while the algorithm works on weighted transactions, in sparse data sets, relative runtime increases by a decrease in minimum support (MinSup) parameter in comparison to the FP-Growth, and memory usage is approximately the same. The variations of runtime and memory usage with the MinSup parameter are opposite for dense data sets.