Spatial data mining is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. In this paper, we explore whether clustering methods have a role to play in spatial data mining. To this end, we develop a new clustering method called CLARANS which is based on randomized search. We also de- velop two spatial data mining algorithms that use CLARANS. Our analysis and experiments show that with the assistance of CLARANS, these two algorithms are very effective and can lead to discoveries that are difficult to find with current spatial data mining algorithms.

Abstract—Spatial data mining is the discovery of interesting relationships and characteristics that may exist implicitly in spatial databases. To this end, this paper has three main contributions. First, we propose a new clustering method called CLARANS, whose aim is to identify spatial structures that may be present in the data. Experimental results indicate that, when compared with existing clustering methods, CLARANS is very efficient and effective. Second, we investigate how CLARANS can handle not only points objects, but also polygon objects efficiently. One of the methods considered, called the IR-approximation, is very efficient in clustering convex and nonconvex polygon objects. Third, building on top of CLARANS, we develop two spatial data mining algorithms that aim to discover relationships between spatial and nonspatial attributes. Both algorithms can discover knowledge that is difficult to find with existing spatial data mining algorithms. Index Terms—Spatial data mining, clustering algorithms, randomized search, computational geometry. æ 1

The number and the size of spatial databases, e.g. for geomarketing, traffic control or environmental studies, are rapidly growing which results in an increasing need for spatial data mining. In this paper, we present new algorithms for spatial characterization and spatial trend analysis. For spatial characterization it is important that class membership of a database object is not only determined by its non-spatial attributes but also by the attributes of objects in its neighborhood. In spatial trend analysis, patterns of change of some non-spatial attributes in the neighborhood of a database object are determined. We present several algorithms for these tasks. These algorithms were implemented within a general framework for spatial data mining providing a small set of database primitives on top of a commercial spatial database management system. A performance evaluation using a real geographic database demonstrates the effectiveness of the proposed algorithms. Furthermore, we show how the algorithms can be combined to discover even more interesting spatial knowledge.

<E-198> In recent years both the number and the size of organisational databases have increased rapidly. However, although available<E-408> processing power has also grown, the increase in stored data has not necessarily led to a corresponding increase in useful<E-418> information and knowledge. This has led to a growing interest in the development of tools capable of harnessing the increased<E-406> processing power available to better utilise the potential of stored data. The terms "Knowledge Discovery in Databases" and<E-426> "Data Mining" have been adopted for a field of research dealing with the automatic discovery of knowledge implicit within<E-418> databases. Data mining is useful in situations where the volume of data is either too large or too complicated for manual<E-414> processing or, to a lesser extent, where human experts are unavailable to provide knowledge. The success already attained by<E-430> a wide range of data mining applications has continued to prompt further in...

: 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 spatial database management system. This will speed up both, the development and the execution of spatial data mining algorithms. In this paper, we define neighborhood graphs and paths and a small set of database primitives for their manipulation. Furthermore, we introduce neighborhood indices to speed up the processing of our database primitives. We implemented the database primitives on top of a commercial spatial database management system. The effectiveness and efficiency of the proposed approach was evaluated by using an analytical cost model and an extensive experimental study on a geographi...

ABSTRACT. This paper reviews the data mining methods that are combined with Geographic Information Systems (GIS) for carrying out spatial analysis of geographic data. We will first look at data mining functions as applied to such data and then highlight their specificity compared with their application to classical data. We will go on to describe the research that is currently going on in this area, pointing out that there are two approaches: the first comes from learning on spatial databases, while the second is based on spatial statistics. We will conclude by discussing the main differences between these two approaches and the elements they have in common.

Abstract. Spatial join is the most expensive operation in geographic databases, but essentially important to compute spatial relationships intrinsic to geographic data. In account of spatial relationships real world entities may affect the behavior of other entities in the neighborhood. Spatial relationships are fundamental for knowledge discovery in geographic databases and are strongly related to the discovered patterns. Knowledge discovery is a user-dependent process, but the user is usually neither an expert in geographic databases nor in spatial relationships. This paper presents an approach to reduce the number of spatial relationships for knowledge discovery, using a geo-ontology and semantic spatial integrity constraints. We show how spatial constraints can help the user of knowledge discovery in both defining the semantically consistent spatial relationships and reducing the verification of unnecessary relationships. 1