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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2013
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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. |
format | Online Article Text |
id | pubmed-3706014 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
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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