High Utility Item-set Mining from retail market data stream with various discount strategies using EGUI-tree

2021 ◽  
Author(s):  
Pandillapalli Amaranatha Reddy ◽  
Munaga Hazarath Murali Krishna Prasad
Keyword(s):  
Data Stream ◽  
Retail Market ◽  
Market Data ◽  
High Utility

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 Predicting seasonal influenza using supermarket retail records

2021 ◽  
Vol 17 (7) ◽  
pp. e1009087
Author(s):  
Ioanna Miliou ◽  
Xinyue Xiong ◽  
Salvatore Rinzivillo ◽  
Qian Zhang ◽  
Giulio Rossetti ◽  
...  
Keyword(s):  
Real Time ◽  
Seasonal Influenza ◽  
Epidemiological Data ◽  
Digital Data ◽  
Support Vector ◽  
Learning Approaches ◽  
Retail Market ◽  
Research Activity ◽  
Market Data ◽  
Real Time Analysis

Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on real-time epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence. Our predictions outperform both a baseline autoregressive model and a second baseline based on product purchases. The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics.

scholarly journals Going Beyond GDP to Nowcast Well-Being Using Retail Market Data

2016 ◽  
pp. 29-42 ◽  
Author(s):  
Riccardo Guidotti ◽  
Michele Coscia ◽  
Dino Pedreschi ◽  
Diego Pennacchioli
Keyword(s):  
Well Being ◽  
Retail Market ◽  
Market Data ◽  
Beyond Gdp

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.  

Evidence-based regulation of labour migration in OECD countries: setting quotas, selection criteria, and shortage lists

2014 ◽  
Vol 11 (1) ◽  
pp. 11-22 ◽  
Author(s):  
Jonathan Chaloff
Keyword(s):  
Labour Market ◽  
Selection Criteria ◽  
Oecd Countries ◽  
Market Analysis ◽  
Multiple Criteria ◽  
Labour Migration ◽  
Evidence Based ◽  
Periodic Review ◽  
Market Data ◽  
Almost All

The growing complexity of selection criteria for discretionary labour migration in OECD countries has been accompanied by an expanded demand for labour market analysis and consultation with stakeholders. While some features of general or detailed criteria may be fixed in legislation, numerical quotas or targets, shortage lists, and multiple-criteria points-based systems are generally subject to periodic review and revision based on labour market data and consultation with stakeholders. Official government bodies have maintained co-ordination of this process, with varying degrees of externalization. In most countries expertise is internal, with recourse to external mandated bodies rare. In almost all cases, however, the process is designed to promote consensus around the policy while maintaining political control.

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