Simultaneous optimisation of clustering quality and approximation error for time series segmentation

Áreas de investigación:

• Methodology - Machine learning

Año:

2018

Tipo de publicación:

Artículo

Palabras clave:

Time series segmentation, multiobjective optimisation, clustering, evolutionary computation

Autores:

• Durán-Rosal, Antonio Manuel
• Gutiérrez, Pedro Antonio
• Martínez-Estudillo, Francisco José
• Hervás-Martínez, César

Journal:

Information Sciences

Volumen:

442-443

Páginas:

186-201

Mes:

Mayo

ISSN:

0020-0255

BibTex:

@article{DuranINS2018, author = "Antonio Manuel Dur{\'a}n-Rosal and Pedro Antonio Guti{\'e}rrez and Francisco Jos{\'e} Mart{\'i}nez-Estudillo and C{\'e}sar Herv{\'a}s-Mart{\'i}nez", abstract = "Time series segmentation is aimed at representing a time series by using a set of segments. Some researchers perform segmentation by approximating each segment with a simple model (e.g. a linear interpolation), while others focus their efforts on obtaining homogeneous groups of segments, so that common patterns or behaviours can be detected. The main hypothesis of this paper is that both objectives are conflicting, so time series segmentation is proposed to be tackled from a multiobjective perspective, where both objectives are simultaneously considered, and the expert can choose the desired solution from a Pareto Front of different segmentations. A specific multiobjective evolutionary algorithm is designed for the purpose of deciding the cut points of the segments, integrating a clustering algorithm for fitness evaluation. The experimental validation of the methodology includes three synthetic time series and three time series from real-world problems. Nine clustering quality assessment metrics are experimentally compared to decide the most suitable one for the algorithm. The proposed algorithm shows good performance for both clustering quality and reconstruction error, improving the results of other mono-objective alternatives of the state-of-the-art and showing better results than a simple weighted linear combination of both corresponding fitness functions.", awards = "JCR(2018): 5.524 Position: 9/155 (Q1) Category: COMPUTER SCIENCE, INFORMATION SYSTEMS", comments = "JCR(2018): 5.524 Position: 9/155 (Q1) Category: COMPUTER SCIENCE, INFORMATION SYSTEMS", doi = "10.1016/j.ins.2018.02.041", issn = "0020-0255", journal = "Information Sciences", keywords = "Time series segmentation, multiobjective optimisation, clustering, evolutionary computation", month = "May", note = "JCR(2018): 5.524 Position: 9/155 (Q1) Category: COMPUTER SCIENCE, INFORMATION SYSTEMS", pages = "186--201", title = "{S}imultaneous optimisation of clustering quality and approximation error for time series segmentation", url = "doi.org/10.1016/j.ins.2018.02.041", volume = "442-443", year = "2018", }

Nota:

JCR(2018): 5.524 Position: 9/155 (Q1) Category: COMPUTER SCIENCE, INFORMATION SYSTEMS

Abstract:

Time series segmentation is aimed at representing a time series by using a set of segments. Some researchers perform segmentation by approximating each segment with a simple model (e.g. a linear interpolation), while others focus their efforts on obtaining homogeneous groups of segments, so that common patterns or behaviours can be detected. The main hypothesis of this paper is that both objectives are conflicting, so time series segmentation is proposed to be tackled from a multiobjective perspective, where both objectives are simultaneously considered, and the expert can choose the desired solution from a Pareto Front of different segmentations. A specific multiobjective evolutionary algorithm is designed for the purpose of deciding the cut points of the segments, integrating a clustering algorithm for fitness evaluation. The experimental validation of the methodology includes three synthetic time series and three time series from real-world problems. Nine clustering quality assessment metrics are experimentally compared to decide the most suitable one for the algorithm. The proposed algorithm shows good performance for both clustering quality and reconstruction error, improving the results of other mono-objective alternatives of the state-of-the-art and showing better results than a simple weighted linear combination of both corresponding fitness functions.

Comentarios:

JCR(2018): 5.524 Position: 9/155 (Q1) Category: COMPUTER SCIENCE, INFORMATION SYSTEMS