Adaptation in Clustering Algorithm by Algorithm Output Granularity for Mobile Data Stream Mining

被引：0

作者：

Wasule, Rahul ^{[1
]}

Fadnavis, R. A. ^{[1
]}

机构：

[1] Yeshwantrao Chavan Coll Engn, Dept Informat Technol, Nagpur, Maharashtra, India

来源：

2014 INTERNATIONAL CONFERENCE ON INFORMATION COMMUNICATION AND EMBEDDED SYSTEMS (ICICES) | 2014年

关键词：

Mobile data mining; Algorithm Output Granularity; k-means;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页数：4

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