The purpose of this paper is to provide an overall view of the methods of spatial data mining and its applications to digital landscape models. Spatial data mining can be defined as the deduction of information that is not explicitly stored in a given spatial data model. The subject spatial data mining represents the integration of several fields, including machine learning, database systems, d...

We describe current research in temporal, spatial, and spatiotemporal data mining. In these types of data mining, a model of time, space, or space-time plays a nontrivial role. As an example of current research, we describe our MegaMiner prototype software. The DGG-Discover 5.2 module of MegaMiner is based on expected distribution domain generalization graphs (EDDGGs), which allow detailed doma...

Spatial data mining requires the analysis of the interactions in space. The conventional data mining algorithms do not support well this type of analysis. We present in this paper an approach based on inductive logic programming (ILP). It is based on two ideas. The first one consists in materializing these interactions using distance tables, so that the spatial data mining problem is reduced to...

Studies on spatial co-location mining required distance threshold to define spatial neighbourhood (Shashi Shekhar and Yan Huang(2001); Yoo and Shekhar (2004, 2006); Yasuhiko Morimoto(2001); Koperski and Han(1995); Ding et al. (2008)) However, it is problematical for users to choose suitable threshold values because they lack prior knowledge about spatial data. Spatial neighbourhood has been def...

1. ABSTRACT In day to day life, vehicles have become important aspects in human life where each vehicle is manufactured for a particular purpose. Co-location pattern discovery is intended towards the processing data with spatial perspectives to determine classes of spatial objects that are frequently located together. Mining co-location patterns from spatial databases may disclose the types of ...

This paper discusses a number of technologies, approaches, and research areas which have been identified as having critical and future promise in the field of data mining. Among the areas which are being developed, investigated, and applications identified for include hypertext and hypermedia data mining, ubiquitous data mining (UDM), phenomenal data mining, distributed and collective data mini...

background and purpose: kidney failure reduces the kidney function and in long term it leads to chronic kidney disease. one of the main complications of this disease is irreversible damage to the kidneys (end-stage kidney disease) and hemodialysis is the main method used to treat advanced kidney failure. the main problem associated with hemodialysis is treating anemia caused by lack of erythrop...