Đang chuẩn bị liên kết để tải về tài liệu:
Data Mining and Knowledge Discovery Handbook, 2 Edition part 87

Data Mining and Knowledge Discovery Handbook, 2 Edition part 87. Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data. Data Mining and Knowledge Discovery Handbook, 2nd Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery. | 840 Shashi Shekhar Pusheng Zhang and Yan Huang One of the fundamental assumptions of statistical analysis is that the data samples are independently generated like successive tosses of coin or the rolling of a die. However in the analysis of spatial data the assumption about the independence of samples is generally false. In fact spatial data tends to be highly self correlated. For example people with similar characteristics occupation and background tend to cluster together in the same neighborhoods. The economies of a region tend to be similar. Changes in natural resources wildlife and temperature vary gradually over space. The property of like things to cluster in space is so fundamental that geographers have elevated it to the status of the first law of geography Everything is related to everything else but nearby things are more related than distant things Tobler 1979 . In spatial statistics an area within statistics devoted to the analysis of spatial data this property is called spatial autocorrelation. For example Figure 43.1 shows the value distributions of an attribute in a spatial framework for an independent identical distribution and a distribution with spatial autocorrelation. Knowledge discovery techniques which ignore spatial autocorrelation typically perform poorly in the presence of spatial data. Often the spatial dependencies arise due to the inherent characteristics of the phenomena under study but in particular they arise due to the fact that the spatial resolution of imaging sensors are finer than the size of the object being observed. For example remote sensing satellites have resolutions ranging from 30 meters e.g. the Enhanced Thematic Mapper of the Landsat 7 satellite of NASA to one meter e.g. the IKONOS satellite from Spaceimaging while the objects under study e.g. Urban Forest Water are often much larger than 30 meters. As a result per-pixel-based classifiers which do not take spatial context into account often produce classified images .