Spatial clustering is an important field of spatial data mining and knowledge discovery that serves to partition a spatial data set to obtain disjoint subsets with spatial elements that are similar to each other. Existing algorithms can be used to perform three types of cluster analyses, including clustering of spatial points, regionalization and point pattern analysis. However, all these exist...

Spatial data mining is the task of discovering knowledge from spatial data. Density-Based Spatial Clustering occupies an important position in spatial data mining task. This paper presents a detailed survey of density-based spatial clustering of data. The various algorithms are described based on DBSCAN comparing them on the basis of various attributes and different pitfalls. The advantages and...

Due to the limitation of the local spatial information in an image, fuzzy c-means clustering algorithms with the local spatial information cannot obtain the satisfying segmentation performance on the image heavily contaminated by noise. In order to compensate this drawback of the local spatial information, an effective kind of non-local spatial information is extracted from the image in this pa...

Clustering geographic data using traditional methods often result in clusters that look dispersed over the geographic space and poorly reflect any underlying spatial structure. We propose a polygon-based spatial clustering approach, which models a spatial object as a polygon with three groups of attributes: general attributes, boundary attributes, and spatial events. We have developed a general...

An evaluation of spatial patterns and a clustering play an important role among methods of spatial statistics. However, traditional clustering techniques are seldom suitable for analyses of spatial data and patterns because they usually do not count on spatial relations and qualities of objects. This paper aims to introduce usage of methods of spatial clustering estimation, which are based main...

The traditional approach of classifying multispectral and hyperspectral imagery begins with clustering in feature space, followed by labeling the classes in the image space. To overcome some of the disadvantages of this sequential approach is to consider spatial constraints during clustering, for example by using Markov Random Fields. We propose in this paper an agent based clustering method as...

In this paper, a novel clustering algorithm is proposed to address the clustering problem within both spatial and non-spatial domains by employing a fusion-based approach. The motivation for this work is to overcome the limitations of the existing spatial clustering methods. In most conventional spatial clustering algorithms, the similarity measurement mainly takes the geometric attributes into...

In this article, we present an algorithm based on genetic algorithm (GA) and R-tree structure to solve a clustering task in spatial data mining. The algorithm is applied to find a cluster for a new spatial object. Spatial objects that represent for each cluster computed dynamically and quickly according to a clustering object in the clustering process. This improves the speed and accuracy of th...