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Reseach Article

Procedural Steps for Knowledge Mining in Time Series

by Kaustuva Chandra Dev, Sibananda Behera
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 63 - Number 13
Year of Publication: 2013
Authors: Kaustuva Chandra Dev, Sibananda Behera
10.5120/10525-5508

Kaustuva Chandra Dev, Sibananda Behera . Procedural Steps for Knowledge Mining in Time Series. International Journal of Computer Applications. 63, 13 ( February 2013), 13-16. DOI=10.5120/10525-5508

@article{ 10.5120/10525-5508,
author = { Kaustuva Chandra Dev, Sibananda Behera },
title = { Procedural Steps for Knowledge Mining in Time Series },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 63 },
number = { 13 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 13-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume63/number13/10525-5508/ },
doi = { 10.5120/10525-5508 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:14:14.355780+05:30
%A Kaustuva Chandra Dev
%A Sibananda Behera
%T Procedural Steps for Knowledge Mining in Time Series
%J International Journal of Computer Applications
%@ 0975-8887
%V 63
%N 13
%P 13-16
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Symbolic intervals which form temporal patterns are usually formulated through Allen's interval relations that originate in temporal reasoning. But this representation is not advantages for knowledge discovery. The Hierarchical Time series Knowledge Representation (HTKR) is the hierarchical language which expresses the temporal aspects of coincidence and partial order, for interval patterns. We present mining procedural steps which are more e?cient, e?ective and based on item set techniques. Pruning of the search space minimizes the mining result size considerably, thereby speeding up the procedural steps and easing the interpretations. When applied on the real data set, HTKR can provide the explanation of underlying temporal phenomena, but whereas the numerous Allen's relation patterns only explains fragmented data.

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Index Terms

Computer Science
Information Sciences

Keywords

Data mining time series knowledge mining temporal relations phrases