scholarly journals Ensemble Classifier for Mining Data Streams

2014 ◽  
Vol 35 ◽  
pp. 397-406 ◽  
Author(s):  
Ireneusz Czarnowski ◽  
Piotr Jędrzejowicz
Keyword(s):  
Data Streams ◽  
Ensemble Classifier ◽  
Mining Data Streams

Mining Data Streams

2014 ◽  
pp. 123-153
Author(s):  
Jure Leskovec ◽  
Anand Rajaraman ◽  
Jeffrey David Ullman
Keyword(s):  
Data Streams ◽  
Mining Data Streams

Mining Data Streams

2006 ◽  
pp. 777-792 ◽  
Author(s):  
Haixun Wang ◽  
Philip S. Yu ◽  
Jiawei Han
Keyword(s):  
Data Streams ◽  
Mining Data Streams

Knowledge Discovery From Evolving Data Streams

2019 ◽  
pp. 19-39
Author(s):  
Prasanna Lakshmi Kompalli
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Data Stream ◽  
Concept Drift ◽  
Data Stream Mining ◽  
Time Data ◽  
Stream Mining ◽  
New Challenges ◽  
Mining Data Streams ◽  
Different Sources

Data coming from different sources is referred to as data streams. Data stream mining is an online learning technique where each data point must be processed as the data arrives and discarded as the processing is completed. Progress of technologies has resulted in the monitoring these data streams in real time. Data streams has created many new challenges to the researchers in real time. The main features of this type of data are they are fast flowing, large amounts of data which are continuous and growing in nature, and characteristics of data might change in course of time which is termed as concept drift. This chapter addresses the problems in mining data streams with concept drift. Due to which, isolating the correct literature would be a grueling task for researchers and practitioners. This chapter tries to provide a solution as it would be an amalgamation of all techniques used for data stream mining with concept drift.

Mining Data Streams

2012 ◽  
pp. 108-138
Author(s):  
Anand Rajaraman ◽  
Jeffrey David Ullman
Keyword(s):  
Data Streams ◽  
Mining Data Streams

Efficient Data Streams Based Closed Frequent Itemsets Mining Algorithm

2012 ◽  
Vol 256-259 ◽  
pp. 2910-2913
Author(s):  
Jun Tan
Keyword(s):  
Data Streams ◽  
Sliding Window ◽  
Frequent Itemsets ◽  
Streaming Data ◽  
Efficient Data ◽  
Closed Itemsets ◽  
Frequent Itemsets Mining ◽  
Synthetic Datasets ◽  
Online Mining ◽  
Mining Data Streams

Online mining of frequent closed itemsets over streaming data is one of the most important issues in mining data streams. In this paper, we proposed a novel sliding window based algorithm. The algorithm exploits lattice properties to limit the search to frequent close itemsets which share at least one item with the new transaction. Experiments results on synthetic datasets show that our proposed algorithm is both time and space efficient.

Mining data streams

2005 ◽  
Vol 34 (2) ◽  
pp. 18-26 ◽  
Author(s):  
Mohamed Medhat Gaber ◽  
Arkady Zaslavsky ◽  
Shonali Krishnaswamy
Keyword(s):  
Data Streams ◽  
Mining Data Streams
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