Spatial Data Mining (SDM) is a complex phenomenon as it deals with data that represents both spatial and non-spatial correlations in spatial databases. SDM extracts latent and implicit trends in spatial data to acquire business intelligence which support expert decision making. Spatial database is very vast as it can hold the spatial objects spread across the globe. Mining such databases have p...

A myriad of applications from different domains collects time series data for further analysis. In many of them, such as seismic datasets, the observed data is also associated to a space dimension, which corresponds, in fact, to spatial-time series. The analysis of these datasets is difficult due to both the continuous nature of the observed data and the relationship between spatial and time di...

Advances in distributed sensing and computing technology offer new, reliable, and costeffective means to collect fine-grained spatiotemporal data. Conventional spatiotemporal data mining procedures, however, are based on centralized models of information processing, where sophisticated and powerful central systems collate and process global information. By contrast, decentralized spatial comput...

− Spatial data mining knows a more and more important interest. Fundamental processes of spatial data mining are in particular clustering and structural patterns detection. These processes are influenced strongly by the concept of proximity or neighborhood. This paper introduces some structures to the construction of a spatial data mining integrating fuzzy structural primitives and propose to o...

Spatial data mining algorithms heavily depend on the efficient processing of neighborhood relations since the neighbors of many objects have to be investigated in a single run of a typical algorithm. Therefore, providing general concepts for neighborhood relations as well as an efficient implementation of these concepts will allow a tight integration of spatial data mining algorithms with a spa...

Movement of university admission is not random. Student admission data can be used to define the likely source of students and movement of the source. Thus, it can help the university improve its courses marketing strategy. Standard database and statistical methods do not work well with interrelated spatial data. The ongoing research presented in this paper attempts to use Geographic Informatio...

Widespread use of spatial databases[24] is leading to an increasing interest in mining interesting and useful but implicit spatial patterns[14, 17, 10, 22]. Efficient tools for extracting information from geo-spatial data, the focus of this work, are crucial to organizations which make decisions based on large spatial data sets. These organizations are spread across many domains including ecolo...