Mining regression models from spatial data is a fundamental task in Spatial Data Mining. We propose a method, namely Mrs-SMOTI, that takes advantage from a tight-integration with spatial databases and mines regression models in form of trees in order to partition the sample space. The method is characterized by three aspects. First, it is able to capture both spatially global and local effects ...

With the increasing availability of spatial data in many applications, spatial clustering and outlier detection has received a lot of attention in the database and data mining community. As a very prominent method, the spatial scan statistic finds a region that deviates (most) significantly from the entire dataset. In this paper, we introduce the novel problem of mining regional outliers in spa...

Data mining is the process of extracting data’s from the database engines. Now a days the road traffic and accidents are main area for the researchers to discover the new problems behind that. It is commonly used in a marketing, inspection, fraud detection and scientific invention. In data mining, machine learning is mainly focused as research which is automatically learnt to recognize complex ...

The research of spatial data is in its infancy stage and there is a need for an accurate method for rule mining. Association rule mining searches for interesting relationships among items in a given data set. This paper enables us to extract pattern from spatial database using k-means algorithm which refers to patterns not explicitly stored in spatial databases. Since spatial association mining...

For mining features from the social web, analysis of the shape, detection of network topology and corresponding special meanings and also clustering of data become tools, because the information obtained by these tools can create useful data behind the social web by revealing its relationships and the relative positions of data. For example, if we want to understand the effect of someone’s stat...

CommonGIS is a system comprising a number of tools for visual data analysis. In this paper we demonstrate our recent developments for analysis of spatial time series data.

Spatial data mining (SDM) and change detection (CD) are hot issues in the field of geospatial information processing. Considering the feasibility of discovering knowledge from spatial database and the demands of knowledge for change detection, the paper presents that change detection needs knowledge and technology of SDM should be integrated into applications of change detection. Special knowle...

The research of spatial data is in its infancy stage and there is a need for an accurate method for rule mining. Association rule mining searches for interesting relationships among items in a

The discovery of preferences in space and time is important in a variety of applications. In this paper we first establish the correspondence between a set of preferences in space and time and density estimates obtained from observations of spatial-temporal features recorded within large databases. We perform density estimation using both kernel methods and mixture models. The density estimates...