Existing methods of spatial data clustering have focused on point data, whose similarity can be easily defined. Due to the complex shapes and alignments of polygons, the similarity between non-overlapping polygons is important to cluster polygons. This study attempts to present an efficient method to discover clustering patterns of polygons by incorporating spatial cognition principles and mult...

In this work we introduce an anisotropic density-based clustering algorithm. It outperforms DBSCAN and OPTICS for the detection of anisotropic spatial point patterns and performs equally well in cases that do not explicitly benefit from an anisotropic perspective. ADCN has the same time complexity as DBSCAN and OPTICS, namely O(n log n) when using a spatial index, O(n2) otherwise. STKO@Geograph...

Title of dissertation: EFFICIENT ALGORITHMS FOR CLUSTERING AND INTERPOLATION OF LARGE SPATIAL DATA SETS Nargess Memarsadeghi Doctor of Philosophy, 2007 Dissertation directed by: Professor David M. Mount Department of Computer Science Categorizing, analyzing, and integrating large spatial data sets are of great importance in various areas such as image processing, pattern recognition, remote sen...

Global clustering has rarely been investigated in the area of spatial dambase systems although dramatic performance improvements can be achieved by using suitable techniques. In this paper, we propose a simple approach to global clustering called cluster organization. We will demonstrate that this cluster organization leads to considerable performance improvements without any algorithmic ovedxa...

Spatial indexing is a well researched field that benefited computer science with many outstanding results. Our effort in this paper can be seen as revisiting some outstanding contributions to spatial indexing, questioning some paradigms, and designing an access method with globally improved performance characteristics. In particular, we argue that dynamic R-tree construction is a typical cluste...

Clustering of spatial data in the presence of obstacles, facilitator and constraints has the very strong practical value, and becomes to an important research issue. Most of the existing spatial clustering algorithm in presence of obstacles and constraints can’t cluster with irregular obstacles. In this paper a 3DCCOM Polygon Reduction Algorithm is proposed. The advantage of this clustering alg...

this paper introduces a novel methodology for the segmentation of brain ms lesions in mri volumes using a new clustering algorithm named scpfcm.  scpfcm uses membership, typicality and spatial information to cluster each voxel. the proposed method relies on an initial segmentation of ms lesions in t1-w and t2-w images by applying scpfcm algorithm, and the t1 image is then used as a mask and is ...

Clustering is the task of grouping a set of objects in such a way that objects are more similar to each other than those in the other groups. Various clustering algorithms were developed, but it ignores the spatial relationship between pixel values then noise can be added to the image and it does not provide edge detection accuracy. Fuzzy local information C-means is the best image clustering m...

introduction one of the critical issues associated with sustainability in the twenty-first century is how cities are growing and developing in the space. rapid urban growth and physical development of cities cause disintegration of the spatial organization of cities and consequently spatial inequality between urban spaces. this phenomenon can be considered as spatial segregation or fragmentatio...