خوشه بندی سری های زمانی
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خوشه بندی سری های زمانی

عنوان فارسی مقاله: کشف u شکل مبتنی بر همبستگی Frobenius برای خوشه بندی سری های زمانی
عنوان انگلیسی مقاله: Frobenius correlation based u-shapelets discovery for time series clustering
مجله/کنفرانس: تشخیص الگو – Pattern Recognition
رشته های تحصیلی مرتبط: مهندسی کامپیوتر
گرایش های تحصیلی مرتبط: مهندسی الگوریتم و محاسبات
کلمات کلیدی فارسی: خوشه بندی، U شکل، همبستگی، سری زمانی
کلمات کلیدی انگلیسی: Clustering, U Shapelet, Correlation, Time series
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus – Master Journals List – JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.patcog.2020.107301
دانشگاه: University Clermont Auvergne, CNRS, LIMOS, F-63000 Clermont-Ferrand, France
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2020
ایمپکت فاکتور: 7.346 در سال 2019
شاخص H_index: 180 در سال 2020
شاخص SJR: 1.363 در سال 2019
شناسه ISSN: 0031-3203
شاخص Quartile (چارک): Q1 در سال 2019
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 38
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E14974
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


۱٫ Introduction


۲٫ Background and related works


۳٫ Our approach


۴٫ Experimental evaluation


۵٫ General conclusion and future work


Declaration of Competing Interest


Acknowlgedgments


Appendix


References

نمونه متن انگلیسی مقاله

Abstract


An u-shapelet is a sub-sequence of a time series used for the clustering of time series datasets. The purpose of this paper is to discover u-shapelets on uncertain time series. To achieve this goal, we propose a dissimilarity score called FOTS whose computation is based on the eigenvector decomposition and the comparison of the autocorrelation matrices of the time series. This score is robust to the presence of uncertainty; it is not very sensitive to transient changes; it allows capturing complex relationships between time series such as oscillations and trends, and it is also well adapted to the comparison of short time series. The FOTS score is used with the Scalable Unsupervised Shapelet Discovery algorithm for the clustering of 63 datasets, and it has shown a substantial improvement in the quality of the clustering with respect to the Rand Index. This work defines a novel framework for the clustering of uncertain time series.


Introduction


All measurements performed by a mechanical system contain uncertainty. Indeed, the uncertainty principle is partly a statement about the limitations of mechanical systems ability to perform measurements on a system without disturbing it [1]. Thus, time series from measurement instruments are uncertain. These time series produced by sensors constitute a vast proportion of the time series used in science, whether in medicine with ECGs, in physics with measurements recorded by telescopes, in computing with the Internet of Things and so on. Ignoring the uncertainty of the data during their analysis can lead to inaccurate conclusions [2], hence the need to implement uncertain data management techniques. Several recent studies have focused on the processing of uncertainty in data mining. Rizvandi et al.[3] studied CPU utilization time patterns of several MapReduce applications using Dynamic Time Warping and Euclidian distance for comparing times series, and they investigated the minimum distance/maximum similarity of these applications. Their results showed the effectiveness of their approach on a private cloud with up to 25 virtual nodes. Considering that time series data often contain uncertainty and that DUST is one of the latest methods that can deal with arbitrary probability distributions, but that its computational cost is high particularly when the dataset is large, Hwang et al. [4] demonstrated that the performance of DUST was much faster using GPU than the CPU-based implementation. Rehfeld and Kurths [5] investigated similarity estimators that could be suitable for the quantitative investigation of dependencies in irregular and age-uncertain time series like paleoclimate time series.

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