Efficiently Finding High Utility-Frequent Itemsets Using Cutoff and Suffix Utility

2019 ◽  
pp. 191-203 ◽  
Author(s):  
R. Uday Kiran ◽  
T. Yashwanth Reddy ◽  
Philippe Fournier-Viger ◽  
Masashi Toyoda ◽  
P. Krishna Reddy ◽  
...  
Keyword(s):  
Frequent Itemsets ◽  
High Utility

scholarly journals Towards Efficient Mining of Periodic High-Utility Itemsets in Large Databases

2019 ◽  
Vol 8 (4) ◽  
pp. 8083-8091
Keyword(s):  
Computational Cost ◽  
Frequent Itemsets ◽  
Experimental Results ◽  
Large Databases ◽  
Very Large Databases ◽  
Frequent Itemsets Mining ◽  
Mining Methods ◽  
High Utility ◽  
High Utility Itemsets ◽  
Temporal Periodicity

High Utility Item sets mining has attracted many researchers in recent years. But HUI mining methods involves a exponential mining space and returns a very large number of high-utility itemsets. . Temporal periodicity of itemset is considered recently as an important interesting criteria for mining high-utility itemsets in many applications. Periodic High Utility item sets mining methods has a limitation that it does not consider frequency and not suitable for large databases. To address this problem, we have proposed two efficient algorithms named FPHUI( mining periodic frequent HUIs), MFPHM(efficient mining periodic frequent HUIs) for mining periodic frequent high-utility itemsets. The first algorithm FPHUI miner generates all periodic frequent itemsets. Mining periodic frequent high-utility itemsets leads to more computational cost in very large databases. We further developed another algorithm called MFPHM to overcome this limitation. The performance of the frequent FPHUI miner is evaluated by conducting experiments on various real datasets. Experimental results show that proposed algorithms is efficient and effective.

scholarly journals Association Mining for Super Market Sales using UP Growth and Top-K Algorithm

2020 ◽  
Vol 32 ◽  
pp. 03012
Author(s):  
Harshal Bhope ◽  
Yash Mahajan ◽  
Swapnil Deore ◽  
Vimla Jethani
Keyword(s):  
Data Mining ◽  
Frequent Itemsets ◽  
Large Datasets ◽  
Association Mining ◽  
Minimum Threshold ◽  
Weighted Utility ◽  
High Utility

Frequent itemsets(HUIs) mining is an evolving field in data mining, that centers around finding itemsets having a utility that meets a user-specified minimum utility by finding all the itemsets. A problem arises in setting up minimum utility exactly which causes difficulties for users. By setting minimum utility underneath average, too many incessant itemsets will be generated, which in turn will make the mining process quite inefficient. No frequent itemsets will be found if the minimum utility is set too huge. The research focuses on generating frequent itemsets by using the transaction weighted utility of each product. While using UP growth methodology for discovering high utility items from large datasets it takes more time and consumes more memory due to which it is less efficient. So to overcome these drawbacks of UP growth we use the Top-K algorithm which makes it more scalable and efficient. Therefore, we use the Top-K algorithm which does not require a minimum threshold.

Discovering Spatial High Utility Frequent Itemsets in Spatiotemporal Databases

2019 ◽  
pp. 287-306
Author(s):  
P. P. C. Reddy ◽  
R. Uday Kiran ◽  
Koji Zettsu ◽  
Masashi Toyoda ◽  
P. Krishna Reddy ◽  
...  
Keyword(s):  
Frequent Itemsets ◽  
Spatiotemporal Databases ◽  
High Utility

scholarly journals Utilizing Index-Based Periodic High Utility Mining to Study Frequent Itemsets

2021 ◽  
Author(s):  
Roy Setiawan ◽  
Dac-Nhuong Le ◽  
Regin Rajan ◽  
Thirukumaran Subramani ◽  
Dilip Kumar Sharma ◽  
...  
Keyword(s):  
Frequent Itemsets ◽  
Utility Mining ◽  
High Utility

Frequent itemsets grouping algorithm based on Hash list

2013 ◽  
Vol 33 (11) ◽  
pp. 3045-3048
Author(s):  
Hongmei WANG ◽  
Ming HU
Keyword(s):  
Frequent Itemsets ◽  
Grouping Algorithm

A differentially private greedy decision forest classification algorithm with high utility

2020 ◽  
Vol 96 ◽  
pp. 101930
Author(s):  
Zhitao Guan ◽  
Xianwen Sun ◽  
Lingyun Shi ◽  
Longfei Wu ◽  
Xiaojiang Du
Keyword(s):  
Classification Algorithm ◽  
Forest Classification ◽  
High Utility ◽  
Decision Forest
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