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

点击翻译按钮获取中文摘要

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.