Mining top-k high-utility itemsets from a data stream under sliding window model

2017 ◽  
Vol 47 (4) ◽  
pp. 1240-1255 ◽  
Author(s):  
Siddharth Dawar ◽  
Veronica Sharma ◽  
Vikram Goyal
Keyword(s):  
Data Stream ◽  
Sliding Window ◽  
High Utility ◽  
High Utility Itemsets

scholarly journals PHUIMUS: A Potential High Utility Itemsets Mining Algorithm Based on Stream Data with Uncertainty

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Ju Wang ◽  
Fuxian Liu ◽  
Chunjie Jin
Keyword(s):  
Data Stream ◽  
Uncertain Data ◽  
Real Life ◽  
Stream Data ◽  
Memory Consumption ◽  
Sliding Windows ◽  
Uncertain Data Streams ◽  
High Utility ◽  
High Utility Itemsets ◽  
Weighted Probability

High utility itemsets (HUIs) mining has been a hot topic recently, which can be used to mine the profitable itemsets by considering both the quantity and profit factors. Up to now, researches on HUIs mining over uncertain datasets and data stream had been studied respectively. However, to the best of our knowledge, the issue of HUIs mining over uncertain data stream is seldom studied. In this paper, PHUIMUS (potential high utility itemsets mining over uncertain data stream) algorithm is proposed to mine potential high utility itemsets (PHUIs) that represent the itemsets with high utilities and high existential probabilities over uncertain data stream based on sliding windows. To realize the algorithm, potential utility list over uncertain data stream (PUS-list) is designed to mine PHUIs without rescanning the analyzed uncertain data stream. And transaction weighted probability and utility tree (TWPUS-tree) over uncertain data stream is also designed to decrease the number of candidate itemsets generated by the PHUIMUS algorithm. Substantial experiments are conducted in terms of run-time, number of discovered PHUIs, memory consumption, and scalability on real-life and synthetic databases. The results show that our proposed algorithm is reasonable and acceptable for mining meaningful PHUIs from uncertain data streams.

An Algorithm of Top-k High Utility Itemsets Mining over Data Stream

2014 ◽  
Vol 9 (9) ◽  
Author(s):  
Tianjun Lu ◽  
Yang Liu ◽  
Le Wang
Keyword(s):  
Data Stream ◽  
High Utility ◽  
High Utility Itemsets

scholarly journals Framework for High Utility Pattern Mining using Dynamically Generated Minimum Support ThresholdFramework for High Utility Pattern Mining using Dynamically Generated Minimum Support Threshold

2018 ◽  
Vol 7 (4.19) ◽  
pp. 1007
Author(s):  
Shankar B. Naik ◽  
Jyoti D. Pawar
Keyword(s):  
Data Stream ◽  
Pattern Mining ◽  
Frequent Pattern Mining ◽  
Sliding Window ◽  
Frequent Pattern ◽  
Minimum Support ◽  
Itemset Mining ◽  
Support Threshold ◽  
High Utility

In this paper we have proposed a framework which uses high utility itemset mining to store data stream elements in a compressed form and then detect events from the sliding window. This approach promises to reduce the memory requirements when applied to frequent pattern mining in data streams.In addition to this, a method to dynamically define the value of minimum support threshold based on data in the data stream is presented.  

An efficient fast algorithm for discovering closed+ high utility itemsets

2016 ◽  
Vol 45 (1) ◽  
pp. 44-74 ◽  
Author(s):  
Jayakrushna Sahoo ◽  
Ashok Kumar Das ◽  
A. Goswami
Keyword(s):  
Fast Algorithm ◽  
High Utility ◽  
High Utility Itemsets

Efficient algorithms for mining closed high utility itemsets in dynamic profit databases

2021 ◽  
Vol 186 ◽  
pp. 115741
Author(s):  
Trinh D.D. Nguyen ◽  
Loan T.T. Nguyen ◽  
Lung Vu ◽  
Bay Vo ◽  
Witold Pedrycz
Keyword(s):  
Efficient Algorithms ◽  
High Utility ◽  
High Utility Itemsets
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