Clustering Models for Data Stream Mining

被引：5

作者：

Mythily, R. ^{[1
]}

Banu, Aisha ^{[2
]}

Raghunathan, Shriram ^{[2
]}

机构：

[1] BS Abdur Rahman Univ, Dept Informat Technol, Madras, Tamil Nadu, India

[2] BS Abdur Rahman Univ, Dept Comp Sci & Engn, Madras, Tamil Nadu, India

来源：

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGIES, ICICT 2014 | 2015年 / 46卷

关键词：

Data streams; information retrieval; data mining;

D O I：

10.1016/j.procs.2015.02.107

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

The scope of this research is to aggregate news contents that exists in data streams. A data stream may have several research issues. A user may only be interested in a subset of these research issues; there could be many different research issues from multiple streams, which discuss similar topic from different perspectives. A user may be interested in a topic but do not know how to collect all feeds related to this topic. The objective is to cluster all stories in the data streams into hierarchical structure for a better serve to the readers. The work utilizes segment wise distributional clustering that show the effectiveness of data streams. To better serve the news readers, advance data organization is highly desired. Once catching a glimpse of the topic, user can browse the returned hierarchy and find other stories/feeds talking about the same topic in the internet. The dynamically changing of stories needs to use the segment wise distributional clustering algorithm to have the capability to process information incrementally. (C) 2015 The Authors. Published by Elsevier B.V.

引用

页码：619 / 626

页数：8

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