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Recent Techniques of Clustering of Time Series Data: A Survey

by Sangeeta Rani, Geeta Sikka
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 52 - Number 15
Year of Publication: 2012
Authors: Sangeeta Rani, Geeta Sikka
10.5120/8282-1278

Sangeeta Rani, Geeta Sikka . Recent Techniques of Clustering of Time Series Data: A Survey. International Journal of Computer Applications. 52, 15 ( August 2012), 1-9. DOI=10.5120/8282-1278

@article{ 10.5120/8282-1278,
author = { Sangeeta Rani, Geeta Sikka },
title = { Recent Techniques of Clustering of Time Series Data: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { August 2012 },
volume = { 52 },
number = { 15 },
month = { August },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume52/number15/8282-1278/ },
doi = { 10.5120/8282-1278 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:52:58.207873+05:30
%A Sangeeta Rani
%A Geeta Sikka
%T Recent Techniques of Clustering of Time Series Data: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 52
%N 15
%P 1-9
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Time-Series clustering is one of the important concepts of data mining that is used to gain insight into the mechanism that generate the time-series and predicting the future values of the given time-series. Time-series data are frequently very large and elements of these kinds of data have temporal ordering. The clustering of time series is organized into three groups depending upon whether they work directly on raw data either in frequency or time domain, indirectly with the features extracted from the raw data or with model built from raw data. In this paper, we have shown the survey and summarization of previous work that investigated the clustering of time series in various application domains ranging from science, engineering, business, finance, economic, health care, to government.

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Index Terms

Computer Science
Information Sciences

Keywords

Clustering Time series data Data mining Dimensionality reduction Distance measure

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