Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful, patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more diicult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatial...

The large amount of spatial data available today demands the use of data mining tools for its analysis. One of the most used data mining techniques is clustering. Several methods for spatial clustering exist, but many consider space as just another variable. We present in this paper a tool particularly suited for spatial clustering: the GeoSOM suite. This tool implements the GeoSOM algorithm, w...

Association rules discovery is a fundamental task in spatial data mining where data are naturally described at multiple levels of granularity. ARES is a spatial data mining system that takes advantage from this taxonomic knowledge on spatial data to mine multi-level spatial association rules. A large amount of rules is typically discovered even from small set of spatial data. In this paper we p...

customers are the most valuable asset of an organization. due to high contest in the business field, it is necessary to regard the customer relationship management (crm) of the enterprise. data mining and machine learning methods been utilized by businesses in recent years in order to improve crm. crm is the strategy for building, managing, and strengthening loyal and long lasting customer rela...

In this paper, our focus in data mining is concerned with the discovery of spatial associations within images. Our work concentrates on the problem of nding associations between visual content in large image databases. Discovering association rules has been the focus of many studies in the last few years. However, for multimedia data such as images or video frames, the algorithms proposed in th...

Data mining is the process of extracting knowledge from large amounts of data. It covers a variety of techniques aimed at discovering diverse types of patterns on the basis of the requirements of the domain. These techniques include association rules mining, classification, cluster analysis and outlier detection. The availability of applications that produce massive amounts of spatial, spatio-t...

Spatial data mining, i.e., mining knowledge from large amounts of spatial data, is a demanding eld since huge amounts of spatial data have been collected in various applications, ranging from remote sensing to geographical information systems (GIS), computer cartography, environmental assessment and planning. The collected data far exceed people's ability to analyze it. Thus, new and e cient me...

Geographic data preprocessing is the most expensive and effort consuming step in the knowledge discovery process, but has received little attention in the literature. For the data mining step, especially for association rule mining, many different algorithms have been proposed. Their main drawback, however, is the huge amount of generated rules, most of which are well known patterns. This paper...

Although uncertainties exist in spatial knowledge discovery, they have not been paid much attention to. In the past years, the most researches of spatial knowledge discovery focused on the methods of data mining and its algorithms. In this paper, uncertainty and its propagation of spatial data are discussed and analysed firstly. Then, uncertainties at various stages of spatial knowledge discove...