Entropy-based fuzzy clustering of interval-valued time series

IF 2.1 4区计算机科学 Q2 STATISTICS & PROBABILITY

Advances in Data Analysis and Classification Pub Date : 2024-03-29 DOI:10.1007/s11634-024-00586-6

Vincenzina Vitale, Pierpaolo D’Urso, Livia De Giovanni, Raffaele Mattera

引用次数: 0

Abstract

This paper proposes a fuzzy C-medoids-based clustering method with entropy regularization to solve the issue of grouping complex data as interval-valued time series. The dual nature of the data, that are both time-varying and interval-valued, needs to be considered and embedded into clustering techniques. In this work, a new dissimilarity measure, based on Dynamic Time Warping, is proposed. The performance of the new clustering procedure is evaluated through a simulation study and an application to financial time series.

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基于熵的区间值时间序列模糊聚类

本文提出了一种基于模糊c -介质的熵正则化聚类方法，以解决将复杂数据分组为区间值时间序列的问题。数据的双重性质，即时变和区间值，需要考虑并嵌入到聚类技术中。本文提出了一种新的基于动态时间翘曲的不相似度度量方法。通过仿真研究和对金融时间序列的应用，对新聚类方法的性能进行了评价。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Advances in Data Analysis and Classification STATISTICS & PROBABILITY-

CiteScore

3.40

自引率

6.20%

发文量

审稿时长

>12 weeks

期刊介绍： The international journal Advances in Data Analysis and Classification (ADAC) is designed as a forum for high standard publications on research and applications concerning the extraction of knowable aspects from many types of data. It publishes articles on such topics as structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering, and pattern recognition methods; strategies for modeling complex data and mining large data sets; methods for the extraction of knowledge from data, and applications of advanced methods in specific domains of practice. Articles illustrate how new domain-specific knowledge can be made available from data by skillful use of data analysis methods. The journal also publishes survey papers that outline, and illuminate the basic ideas and techniques of special approaches.