Spatial clustering of spatial correlated data, multi-valued data, and domain-driven spatial clustering ensemble

空间相关数据的空间聚类、多值数据和领域驱动的空间聚类集成

• 批准号: 355996-2013
• 负责人: Wang, Xin
• 金额: $ 1.09万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=595771
• 关键词: Spatial; clustering; spatial; correlated; data

Spatial clustering partitions similar data objects into the same group (called clusters) based on their distance, density and reachability in space. The primary goal of this study is to design, develop and improve a set of spatial clustering methods and processes that discover meaningful and useful clusters from large amounts of spatial correlated data, multi-valued spatial data and multi-featured spatial data. Specifically, the research will address the following spatial clustering problems: 1) Spatial correlation generally exists in spatial datasets. It indicates the dependency between the spatial and non-spatial attributes, with the chance that some cause and effect lead to it. The research will identify different unbiased measures of non-spatial attribute similarity and spatial correlation. Then the measures will be integrated into different clustering methods. 2) In many applications, the non-spatial attribute of a spatial object can be represented by a set of multiple values in the d-dimensional space. The object is called a multi-valued spatial object. How to discover the clusters of multi-valued spatial objects is a challenging task. One outcome of this research is to define different similarity measures for multi-valued spatial objects. Different spatial clustering methods will be developed based on the measures. 3) Most existing clustering algorithms do not consider the domain knowledge during the clustering process, which prevents users from precisely describing their goals and understanding the clustering results. In addition, for many applications, none of the current clustering methods is suitable due to the multiple features of the spatial data. Each clustering method may cluster the data from a particular feature or may be suitable for part of the spatial area. How to improve the clustering result for spatial data with multiple features with the aid of domain knowledge is an interesting research topic. This research will first study how to improve the spatial clustering ontology. The new domain-driven spatial cluster methods and cluster ensemble methods will then be proposed to combine multiple clustering results to produce useful and meaningful results.

空间聚类将相似的数据对象根据它们在空间中的距离、密度和可达性划分为同一组（称为聚类）。本研究的主要目标是设计、开发和改进一套空间聚类方法和过程，从大量的空间相关数据、多值空间数据和多特征空间数据中发现有意义和有用的聚类。具体而言，本研究将解决以下空间聚类问题：1）空间数据集普遍存在空间相关性。它表明了空间和非空间属性之间的依赖性，与一些原因和影响的机会导致它。研究将确定不同的无偏测量的非空间属性的相似性和空间相关性。然后将这些度量集成到不同的聚类方法中。2)在许多应用中，空间对象的非空间属性可以由d维空间中的多个值的集合来表示。该对象被称为多值空间对象。如何发现多值空间对象的簇是一个具有挑战性的任务。本研究的成果之一是定义不同的相似性措施，多值空间对象。将根据这些措施制定不同的空间聚类方法。3)现有的聚类算法在聚类过程中大多没有考虑领域知识，这使得用户无法准确地描述自己的目标和理解聚类结果。此外，对于许多应用程序，没有一个当前的聚类方法是合适的，由于空间数据的多个功能。每种聚类方法可以从特定特征聚类数据，或者可以适用于部分空间区域。如何利用领域知识提高多特征空间数据的聚类效果是一个值得研究的课题。本研究将首先研究如何改进空间聚类本体。新的领域驱动的空间聚类方法和集群集成方法，然后将提出联合收割机多个聚类结果，产生有用的和有意义的结果。