Detecting Clusters with Mean shifts by using variable scan window

• Main Authors: Yi-Chun Shiu, 徐逸群
• Other Authors: Chen-ju Lin
• Format: Others
• Language: zh-TW
• Online Access: http://ndltd.ncl.edu.tw/handle/23964281979624439502

碩士 === 元智大學 === 工業工程與管理學系 === 101 === The goal of this research is analyzing spatial and spatiotemporal data. We developed spatial and spatiotemporal scan statistics to detect the regions where the mean values shift and there time of occurrence. Different from the common spatial and spatiotemporal scan statistics that apply circular scan windows with fixed sizes, the proposed method of this research does not require fixed scan radii. Instead, variable scan windows along with standardization were designed to detect the potential abnormal regions. The proposed method is better applicable to the real problems that the size of abnormal region is often unknown. Three methods were developed for spatial analysis: (1) concentric average method: average the observations in each circular scan window, (2) concentric weighted method: weight the observations by distance in each circular scan window, (3) irregular scan window method: average the observations in each irregular scan window. Last, the maximum scan statistics of the scan windows are used to determine whether there exists abnormal region with mean shifts. The three methods are extended to analyzing spatiotemporal data by using the exponentially weighted moving average (EWMA) technique across the temporal axis. Concentric average of the time-weighted method, concentric weighted of the time-weighted method, and irregular scan window method of the time-weighted method were developed to determine the range and time of occurrence of abnormal regions. The simulation results show that the proposed methods can effectively detect the abnormal regions. The proposed methods could be better or worse than the existing methods depending on the pattern of abnormal regions. Last, an implementation of male thyroid cancer in New Mexico is carried out to demonstrate the practicability of the proposed methods.