mHUIMiner: A Fast High Utility Itemset Mining Algorithm for Sparse Datasets

Author(s):  
Alex Yuxuan Peng ◽  
Yun Sing Koh ◽  
Patricia Riddle
2015 ◽  
Vol 49 (1) ◽  
pp. 315-340 ◽  
Author(s):  
Wei Song ◽  
Zihan Zhang ◽  
Jinhong Li

2013 ◽  
Vol 760-762 ◽  
pp. 1713-1717
Author(s):  
Yi Pan ◽  
Bo Zhang

Owing to their major contribution to the total transaction's sales profits, increasingly importance has been attached to high utility itemsets mining. This paper has proposed a TIFF-tree based algorithm, which takes two-pass database scan to obtain the transaction utility information, the conditional matrix of potential high utility is adopted, through the row-column operation, the calculation of transaction utility can be simplified. The experiment result analysis shows that as the decreasing of user-defined threshold, the performance of TIFP-Growth algorithm is much better than the two-phase algorithm.


2018 ◽  
Vol 132 ◽  
pp. 918-927 ◽  
Author(s):  
Krishan Kumar Sethi ◽  
Dharavath Ramesh ◽  
Damodar Reddy Edla

2021 ◽  
pp. 107422
Author(s):  
Jerry Chun-Wei Lin ◽  
Youcef Djenouri ◽  
Gautam Srivastava ◽  
Unil Yun ◽  
Philippe Fourier-Viger
Keyword(s):  

Author(s):  
Amit Verma ◽  
Siddharth Dawar ◽  
Raman Kumar ◽  
Shamkant Navathe ◽  
Vikram Goyal
Keyword(s):  

Author(s):  
Jimmy Ming-Tai Wu ◽  
Qian Teng ◽  
Shahab Tayeb ◽  
Jerry Chun-Wei Lin

AbstractThe high average-utility itemset mining (HAUIM) was established to provide a fair measure instead of genetic high-utility itemset mining (HUIM) for revealing the satisfied and interesting patterns. In practical applications, the database is dynamically changed when insertion/deletion operations are performed on databases. Several works were designed to handle the insertion process but fewer studies focused on processing the deletion process for knowledge maintenance. In this paper, we then develop a PRE-HAUI-DEL algorithm that utilizes the pre-large concept on HAUIM for handling transaction deletion in the dynamic databases. The pre-large concept is served as the buffer on HAUIM that reduces the number of database scans while the database is updated particularly in transaction deletion. Two upper-bound values are also established here to reduce the unpromising candidates early which can speed up the computational cost. From the experimental results, the designed PRE-HAUI-DEL algorithm is well performed compared to the Apriori-like model in terms of runtime, memory, and scalability in dynamic databases.


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