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A Survey on Clustering Algorithms for Data Streams

by Neha Sharma, Shraddha Masih, Pawan Makhija
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 22
Year of Publication: 2018
Authors: Neha Sharma, Shraddha Masih, Pawan Makhija
10.5120/ijca2018918014

Neha Sharma, Shraddha Masih, Pawan Makhija . A Survey on Clustering Algorithms for Data Streams. International Journal of Computer Applications. 182, 22 ( Oct 2018), 18-24. DOI=10.5120/ijca2018918014

@article{ 10.5120/ijca2018918014,
author = { Neha Sharma, Shraddha Masih, Pawan Makhija },
title = { A Survey on Clustering Algorithms for Data Streams },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2018 },
volume = { 182 },
number = { 22 },
month = { Oct },
year = { 2018 },
issn = { 0975-8887 },
pages = { 18-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number22/30066-2018918014/ },
doi = { 10.5120/ijca2018918014 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:12:08.856167+05:30
%A Neha Sharma
%A Shraddha Masih
%A Pawan Makhija
%T A Survey on Clustering Algorithms for Data Streams
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 22
%P 18-24
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data stream mining is an emerging area for extracting useful information from continuous arriving data. Web click stream, weather monitoring, network traffic, shopping history, web log are some key resources of generating data stream. Clustering is one of the most useful technique for analsing stream data, as it does not require any predefined class labeling. Data stream mining is challanging as the data is massive and arriving continuously. The traditional clustering algorithms cannot be directly applied on the data streams. Data stream mining needs one scan algorithms to extract rich data in the form of data streams. In this paper we discuss various data stream clustering algorithms with their limitations and required data structures. This paper also provides a comparative study of these algorithms. Real world applications of data streams, data resources and publicly available softwares are also discussed.

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Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Data Stream Clustering

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