结构变化分析的时间预拓扑概念:在计量经济学中的应用

IF 0.5 4区 计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Nazha Selmaoui-Folcher, Jannaï Tokotoko, Samuel Gorohouna, Laïsa Roi, C. Leschi, Catherine Ris
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引用次数: 0

摘要

预拓扑学是一种从拓扑公理的弱化发展而来的数学模型。它最初用于经济、社会和生物科学,然后用于模式识别和图像分析。最近,它被应用于复杂网络的分析。Pretopology允许在具有弱性质的数学框架中工作,它的非幂等算子称为伪闭包,允许实现迭代算法。它提出了一种一般化图论概念并允许对问题进行普遍建模的形式主义。在本文中,作者将这一数学模型扩展到分析具有时空维度的复杂数据。作者基于时间函数定义了时间预拓扑的概念。他们给出了一个基于二元关系的时间函数的例子,并构造了一个时间预拓扑。他们定义了两个新的时间子结构概念,旨在表示子结构的演化。他们提出了提取这些子结构的算法。他们在两个数据和两个经济真实数据上对这个命题进行了实验。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Concept of Temporal Pretopology for the Analysis for Structural Changes: Application to Econometrics
Pretopology is a mathematical model developed from a weakening of the topological axiomatic. It was initially used in economic, social and biological sciences and next in pattern recognition and image analysis. More recently, it has been applied to the analysis of complex networks. Pretopology enables to work in a mathematical framework with weak properties, and its nonidempotent operator called pseudo-closure permits to implement iterative algorithms. It proposes a formalism that generalizes graph theory concepts and allows to model problems universally. In this paper, authors will extend this mathematical model to analyze complex data with spatiotemporal dimensions. Authors define the notion of a temporal pretopology based on a temporal function. They give an example of temporal function based on a binary relation, and construct a temporal pretopology. They define two new notions of temporal substructures which aim at representing evolution of substructures. They propose algorithms to extract these substructures. They experiment the proposition on 2 data and two economic real data.
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来源期刊
International Journal of Data Warehousing and Mining
International Journal of Data Warehousing and Mining COMPUTER SCIENCE, SOFTWARE ENGINEERING-
CiteScore
2.40
自引率
0.00%
发文量
20
审稿时长
>12 weeks
期刊介绍: The International Journal of Data Warehousing and Mining (IJDWM) disseminates the latest international research findings in the areas of data management and analyzation. IJDWM provides a forum for state-of-the-art developments and research, as well as current innovative activities focusing on the integration between the fields of data warehousing and data mining. Emphasizing applicability to real world problems, this journal meets the needs of both academic researchers and practicing IT professionals.The journal is devoted to the publications of high quality papers on theoretical developments and practical applications in data warehousing and data mining. Original research papers, state-of-the-art reviews, and technical notes are invited for publications. The journal accepts paper submission of any work relevant to data warehousing and data mining. Special attention will be given to papers focusing on mining of data from data warehouses; integration of databases, data warehousing, and data mining; and holistic approaches to mining and archiving
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