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PRESEE: An MDL/MML Algorithm to Time-Series Stream Segmenting

Time-series stream is one of the most common data types in data mining field. It is prevalent in fields such as stock market, ecology, and medical care. Segmentation is a key step to accelerate the processing speed of time-series stream mining. Previous algorithms for segmenting mainly focused on th...

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Detalles Bibliográficos
Autores principales: Xu, Kaikuo, Jiang, Yexi, Tang, Mingjie, Yuan, Changan, Tang, Changjie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3706014/
https://www.ncbi.nlm.nih.gov/pubmed/23956693
http://dx.doi.org/10.1155/2013/386180
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author Xu, Kaikuo
Jiang, Yexi
Tang, Mingjie
Yuan, Changan
Tang, Changjie
author_facet Xu, Kaikuo
Jiang, Yexi
Tang, Mingjie
Yuan, Changan
Tang, Changjie
author_sort Xu, Kaikuo
collection PubMed
description Time-series stream is one of the most common data types in data mining field. It is prevalent in fields such as stock market, ecology, and medical care. Segmentation is a key step to accelerate the processing speed of time-series stream mining. Previous algorithms for segmenting mainly focused on the issue of ameliorating precision instead of paying much attention to the efficiency. Moreover, the performance of these algorithms depends heavily on parameters, which are hard for the users to set. In this paper, we propose PRESEE (parameter-free, real-time, and scalable time-series stream segmenting algorithm), which greatly improves the efficiency of time-series stream segmenting. PRESEE is based on both MDL (minimum description length) and MML (minimum message length) methods, which could segment the data automatically. To evaluate the performance of PRESEE, we conduct several experiments on time-series streams of different types and compare it with the state-of-art algorithm. The empirical results show that PRESEE is very efficient for real-time stream datasets by improving segmenting speed nearly ten times. The novelty of this algorithm is further demonstrated by the application of PRESEE in segmenting real-time stream datasets from ChinaFLUX sensor networks data stream.
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spelling pubmed-37060142013-08-16 PRESEE: An MDL/MML Algorithm to Time-Series Stream Segmenting Xu, Kaikuo Jiang, Yexi Tang, Mingjie Yuan, Changan Tang, Changjie ScientificWorldJournal Research Article Time-series stream is one of the most common data types in data mining field. It is prevalent in fields such as stock market, ecology, and medical care. Segmentation is a key step to accelerate the processing speed of time-series stream mining. Previous algorithms for segmenting mainly focused on the issue of ameliorating precision instead of paying much attention to the efficiency. Moreover, the performance of these algorithms depends heavily on parameters, which are hard for the users to set. In this paper, we propose PRESEE (parameter-free, real-time, and scalable time-series stream segmenting algorithm), which greatly improves the efficiency of time-series stream segmenting. PRESEE is based on both MDL (minimum description length) and MML (minimum message length) methods, which could segment the data automatically. To evaluate the performance of PRESEE, we conduct several experiments on time-series streams of different types and compare it with the state-of-art algorithm. The empirical results show that PRESEE is very efficient for real-time stream datasets by improving segmenting speed nearly ten times. The novelty of this algorithm is further demonstrated by the application of PRESEE in segmenting real-time stream datasets from ChinaFLUX sensor networks data stream. Hindawi Publishing Corporation 2013-06-20 /pmc/articles/PMC3706014/ /pubmed/23956693 http://dx.doi.org/10.1155/2013/386180 Text en Copyright © 2013 Kaikuo Xu et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Xu, Kaikuo
Jiang, Yexi
Tang, Mingjie
Yuan, Changan
Tang, Changjie
PRESEE: An MDL/MML Algorithm to Time-Series Stream Segmenting
title PRESEE: An MDL/MML Algorithm to Time-Series Stream Segmenting
title_full PRESEE: An MDL/MML Algorithm to Time-Series Stream Segmenting
title_fullStr PRESEE: An MDL/MML Algorithm to Time-Series Stream Segmenting
title_full_unstemmed PRESEE: An MDL/MML Algorithm to Time-Series Stream Segmenting
title_short PRESEE: An MDL/MML Algorithm to Time-Series Stream Segmenting
title_sort presee: an mdl/mml algorithm to time-series stream segmenting
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3706014/
https://www.ncbi.nlm.nih.gov/pubmed/23956693
http://dx.doi.org/10.1155/2013/386180
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