Mixed Fuzzy Clustering for Misaligned Time Series

Data mining in medical databases often involves the comparison of time series, which represent the evolution of a physiological variable. Temporal misalignment of physiological variables can conceal the discovery of patterns and trends shared between different patients. To address this problem, this...

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Vydáno v:IEEE transactions on fuzzy systems Ročník 25; číslo 6; s. 1777 - 1794
Hlavní autoři: Salgado, Catia M., Ferreira, Marta C., Vieira, Susana M.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.12.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Classification
Clustering
Clustering algorithms
Clusters
Data mining
Demographics
Dynamic time warping (DTW)
fuzzy clustering
Heuristic algorithms
intensive care unit (ICU)
Invariants
Machine learning
Medical services
misaligned time series
Misalignment
Partitioning algorithms
Patients
Physiology
Time series
Time series analysis
ISSN:1063-6706, 1941-0034
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Shrnutí:Data mining in medical databases often involves the comparison of time series, which represent the evolution of a physiological variable. Temporal misalignment of physiological variables can conceal the discovery of patterns and trends shared between different patients. To address this problem, this paper proposes the mixed fuzzy clustering (MFC) algorithm with the dynamic time-warping (DTW) distance. We developed the MFC algorithm by 1) incorporating the DTW distance into the standard fuzzy c-means to handle misaligned time series; 2) introducing a new dimension into the spatiotemporal clustering algorithm to handle P time-variant features; and 3) incorporating unsupervised learning of cluster-dependent attribute weights. The algorithm is designed to simultaneously cluster time-variant and time-invariant data. We demonstrate the advantages of the proposed algorithm in four synthetic datasets and in two real-world applications in intensive care units. The first application is the classification of patients who will need the administration of vasopressors, and the second is the classification of patients with a high risk of mortality. Time-variant features consist of physiological variables collected with different sampling rates at different points in time. Time-invariant features consist of patients' demographics and score records. The performance is evaluated using cluster validity measures, showing that the proposed algorithm outperforms fuzzy c-means.
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ISSN:1063-6706
1941-0034
DOI:10.1109/TFUZZ.2016.2633375