Online data stream Mining of Recent Frequent Itemsets based on Sliding Window model

2008 ◽  
Author(s):  
Jia-dong Ren ◽  
Ke Li
Keyword(s):  
Data Stream ◽  
Sliding Window ◽  
Frequent Itemsets ◽  
Data Stream Mining ◽  
Stream Mining ◽  
Online Data

scholarly journals Critical evaluation of classifiers in data stream mining

2018 ◽  
Vol 7 (4) ◽  
pp. 2166
Author(s):  
Lalit Agrawal ◽  
Dattatraya Adane
Keyword(s):  
Data Stream ◽  
Forest Cover ◽  
Critical Evaluation ◽  
High Volume ◽  
Data Stream Mining ◽  
Volume Data ◽  
Stream Mining ◽  
Full Data ◽  
Online Data ◽  
Benchmark Datasets

Over past decade there has been a significant increase in the volume of online data. Extracting meaningful knowledge from this high volume data is considered as important aspect of research. It is very difficult to completely store full data, because of its perpetual nature. Therefore, analysis is needed while the “data is moving”. This moving data is known as data stream and analyzing it without storing it completely is termed as data stream mining. In recent years, many new techniques have been proposed to overcome the challenges of data stream mining. In this paper, we review the operation of popular streaming algorithms highlighting their strength and weaknesses. We also evaluate the classifiers used in these algorithms against two popular benchmark datasets namely (a) forest cover (forest) and (b) german credit available at UCI repository. Finally, we present our critical observation and draw conclusions on the basis of our analysis.  

scholarly journals An Adaptive Slide Window Security Method for Transaction Updation in Data Stream Mining

2019 ◽  
Vol 9 (2) ◽  
pp. 2864-2869
Keyword(s):  
Data Stream ◽  
Sliding Window ◽  
Data Stream Mining ◽  
Input Stream ◽  
Stream Mining ◽  
Distributed Information ◽  
Adaptive Window ◽  
Windowing Technique ◽  
Window Approach ◽  
Research Domain

Data steam mining has gained large interest in current research domain. Where various information’s are retrieved based on the content of the context, the accuracy of the input stream with respect to its privacy is a major challenge. Windowing technique is used an effective approach in providing security measure in data stream mining. The recent develop windowing approach operates using sliding window, where anonymity is focused by different processing rules. The linear search sliding window has a constraint of search overhead and loss of generality under distributed information. In this paper, a new adaptive window approach for privacy coding in data stream mining is proposed. This presented approach is developed with the concern of minimize the search overhead and accuracy in search mining performance using adaptive window monitoring

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