DocumentCode
260731
Title
Adaptation in clustering algorithm by algorithm output granularity for mobile data stream mining
Author
Wasule, Rahul ; Fadnavis, R.A.
Author_Institution
Dept. of Inf., Technol., Yeshwantrao Chavan Coll. of Eng., Nagpur, India
fYear
2014
fDate
27-28 Feb. 2014
Firstpage
1
Lastpage
4
Abstract
This paper presents an overview of the current state-of-the-art in mobile data stream mining and its applications. The paper presents the strategies and techniques for adaptation that are essential in order to perform real-time, continuous data mining on mobile devices. We present an overview of adaptation strategies for data stream mining and in particular for memory conservation with Algorithm Output Granularity. For mining purpose, we uses k-means clustering algorithm.
Keywords
data mining; mobile computing; pattern clustering; adaptation strategies; algorithm output granularity; continuous data mining; k-means clustering algorithm; memory conservation; mobile data stream mining; mobile devices; Classification algorithms; Clustering algorithms; Data mining; Educational institutions; Mobile communication; Mobile handsets; Real-time systems; Algorithm Output Granularity; Mobile data mining; k-means;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Communication and Embedded Systems (ICICES), 2014 International Conference on
Conference_Location
Chennai
Print_ISBN
978-1-4799-3835-3
Type
conf
DOI
10.1109/ICICES.2014.7033790
Filename
7033790
Link To Document