Extracting the Boundaries of Clusters: A Post-Clustering Tool for Spatial Datasets

Kayaligil, Sinan
Özdemirel, Nur Evin
Boundary extraction is a fundamental post-clustering problem. It facilitates interpretability and usability of clustering results. Also, it provides visualization and dataset reduction. However, it has not attracted much attention compared to the clustering problem itself. In this work, we address the boundary extraction of clusters in 2- and 3-dimensional spatial datasets. We propose two algorithms based on Delaunay Triangulation (DT). Numerical experiments show that the proposed algorithms generate the cluster boundaries effectively. Also, they yield significant amounts of dataset reduction.


On output independence and complementariness in rank-based multiple classifier decision systems
Saranlı, Afşar (Elsevier BV, 2001-12-01)
This study presents a theoretical analysis of output independence and complementariness between classifiers in a rank-based multiple classifier decision system in the context of the partitioned observation space theory. To enable such an analysis, an information theoretic interpretation of a rank-based multiple classifier system is developed and basic concepts from information theory are applied to develop measures for output independence and complementariness. It is shown that output independence of classi...
Relative consistency of projective reconstructions obtained from the same image pair
Otlu, Burcak; Atalay, Mustafa Ümit; Hassanpour, Reza (World Scientific Pub Co Pte Lt, 2006-08-01)
This study obtains projective reconstructions of an object or a scene from its image pair and measures relative consistency of these projective reconstructions. 3D points are estimated from an image pair using projective and epipolar geometry. Two measures are presented for verification of projective reconstructions with each other. These measures are based on the equality of ratios between the x-, y- and z-coordinates of 3D reconstructed points which are obtained from the same corresponding points. This in...
Almost autonomous training of mixtures of principal component analyzers
Musa, MEM; de Ridder, D; Duin, RPW; Atalay, Mehmet Volkan (Elsevier BV, 2004-07-02)
In recent years, a number of mixtures of local PCA models have been proposed. Most of these models require the user to set the number of submodels (local models) in the mixture and the dimensionality of the submodels (i.e., number of PC's) as well. To make the model free of these parameters, we propose a greedy expectation-maximization algorithm to find a suboptimal number of submodels. For a given retained variance ratio, the proposed algorithm estimates for each submodel the dimensionality that retains th...
SASI: a generic texture descriptor for image retrieval
Carkacioglu, A; Yarman-Vural, F (Elsevier BV, 2003-11-01)
In this paper, a generic texture descriptor, namely, Statistical Analysis of Structural Information (SASI) is introduced as a representation of texture. SASI is based on statistics of clique autocorrelation coefficients, calculated over structuring windows. SASI defines a set of clique windows to extract and measure various structural properties of texture by using a spatial multi-resolution method. Experimental results, performed on various image databases, indicate that SASI is more successful then the Ga...
Undesirable effects of output normalization in multiple classifier systems
Altincay, H; Demirekler, Mübeccel (Elsevier BV, 2003-06-01)
Incomparability of the classifier output scores is a major problem in the combination of different classification systems. In order to deal with this problem, the measurement level classifier outputs are generally normalized. However, empirical results have shown that output normalization may lead to some undesirable effects. This paper presents analyses for some most frequently used normalization methods and it is shown that the main reason for these undesirable effects of output normalization is the dimen...
Citation Formats
T. İNKAYA, S. Kayaligil, and N. E. Özdemirel, “Extracting the Boundaries of Clusters: A Post-Clustering Tool for Spatial Datasets,” INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, pp. 0–0, 2020, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/35246.