The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Data Stream Mining Using Ensemble Classifier: A Collaborative Approach of Classifiers
|
|
Author(s): Snehlata Sewakdas Dongre (Ghrce Nagpur, India)and Latesh G. Malik (Ghrce Nagpur, India)
Copyright: 2017
Pages: 14
Source title:
Collaborative Filtering Using Data Mining and Analysis
Source Author(s)/Editor(s): Vishal Bhatnagar (Ambedkar Institute of Advanced Communication Technologies and Research, India)
DOI: 10.4018/978-1-5225-0489-4.ch013
Purchase
|
Abstract
A data stream is giant amount of data which is generated uncontrollably at a rapid rate from many applications like call detail records, log records, sensors applications etc. Data stream mining has grasped the attention of so many researchers. A rising problem in Data Streams is the handling of concept drift. To be a good algorithm it should adapt the changes and handle the concept drift properly. Ensemble classification method is the group of classifiers which works in collaborative manner. Overall this chapter will cover all the aspects of the data stream classification. The mission of this chapter is to discuss various techniques which use collaborative filtering for the data stream mining. The main concern of this chapter is to make reader familiar with the data stream domain and data stream mining. Instead of single classifier the group of classifiers is used to enhance the accuracy of classification. The collaborative filtering will play important role here how the different classifiers work collaborative within the ensemble to achieve a goal.
Related Content
|
.
© 2023.
34 pages.
|
|
.
© 2023.
15 pages.
|
|
.
© 2023.
15 pages.
|
|
.
© 2023.
18 pages.
|
|
.
© 2023.
24 pages.
|
|
.
© 2023.
32 pages.
|
|
.
© 2023.
21 pages.
|
|
|