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Parvathi, R.
- On 3-dimensional Extended Index Matrices
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PDF Views:258
Authors
Affiliations
1 Professor Asen Zlatarov University, Professor Yakimov Blvd, Bourgas 8000, BG
2 Department of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Street, Bl. 105, Sofia-1113, BG
3 Department of Mathematics, Vellalar College for Women, Erode – 638012, Tamilnadu, IN
1 Professor Asen Zlatarov University, Professor Yakimov Blvd, Bourgas 8000, BG
2 Department of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Acad. G. Bonchev Street, Bl. 105, Sofia-1113, BG
3 Department of Mathematics, Vellalar College for Women, Erode – 638012, Tamilnadu, IN
Source
ScieXplore: International Journal of Research in Science, Vol 1, No 2 (2014), Pagination: 64-68Abstract
An extension of the concept of an Index Matrix (IM), called extended index matrices (EIMs) is introduced. Different operations over EIMs are also introduced.Keywords
Index Matrix Representation, Operations, Relations.Full Text
References
- Atanassov, K., “Conditions in generalized nets”, Proc. of the XIII Spring Conf. of the Union of Bulg. Math., Sunny Beach., pp. 219–226, Apr 1984.
- Atanassov, K., “Generalized index matrices”, Compt. Rend. de l’Academie Bulg. Des. Sci., Vol. 40(11), 1987, p.15–18.
- Atanassov K., “Intuitionistic fuzzy interpretations of multi-criteria, multi-person and multi-measurement tool decision making”, Internat J. Syst Sci., pp. 859–868, 2005.
- Atanassov K., “On Intuitionistic Fuzzy Sets Theory”, Springer, Berlin, 2012.
- Atanassov K., “Extended index matrices”, 7th IEEE International Conference on Intelligent Systems, Warsaw, 24–26 Sept. 2014 (in press).
- Atanassov K., “On index matrices. Part 5: 3-dimensional index matrices”. Advanced Studies in Contemporary Mathematics (in press).
- Atanassov K., “Index Matrices: Towards an Augmented Matrix Calculus”, Springer, Berlin,2014 (in press).
- Atanassov K.E., Szmidt E., Kacprzyk J., “On intuitionistic fuzzy pairs”, Notes on Intuitionistic Fuzzy Sets, Vol. 19(3), pp.1–13. 2013.
- Atanassov E., Szmidt E., Kacprzyk J., Bureva V., “Two examples for the use of 3-dimensional intuitionistic fuzzy index matrices”, Notes on Intuitionistic Fuzzy Sets, Vol. 20(2), pp. 52–59, 2014.
- On Measuring the Reliability of K-Mediods with Obstacles, Facilitators Constraints and Edge Detection on Spatial Clustering
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The Edge detection based K-Mediods algorithms can not only given attention to higher speed and stronger global optimum search, but also get down to the obstacles and facilitator constraints and practicalities of spatial clustering. Taking into account these constraints during the clustering process is costly and the modeling of the constraints is paramount for good performance. The results on real datasets shown that the Edge detection based spatial clustering with the constraints are performs better than the existing constraint based clustering.
Authors
Affiliations
1 Department of Master of Computer Applications, VLB Janakiammal College of Engineering and Technology, Coimbatore, IN
2 Department of Science and Humanities, VLB Janakiammal College of Engineering and Technology, Coimbatore, IN
1 Department of Master of Computer Applications, VLB Janakiammal College of Engineering and Technology, Coimbatore, IN
2 Department of Science and Humanities, VLB Janakiammal College of Engineering and Technology, Coimbatore, IN
Source
Data Mining and Knowledge Engineering, Vol 1, No 7 (2009), Pagination: 364-369Abstract
Clustering spatial data is a well-known problem that has been extensively studied. Grouping similar data in large 2-dimensional spaces to find hidden patterns or meaningful sub-groups has many applications such as satellite images, geographic information systems, medical image analysis, marketing, computer visions, etc. Spatial clustering has been an active research area in Spatial Data Mining (SDM). Many methods on spatial clustering have been proposed in the literature, but few of them have taken into account constraints that may be present in the data clustering. In this paper, we discuss the problem of spatial clustering with obstacles constraints and propose a novel spatial clustering using edge detection method and K-Mediods, which objective is to cluster the spatial data (images) with the constraints and also comparing the result with the various constraints based clustering algorithms in terms of number of clusters and its execution time.The Edge detection based K-Mediods algorithms can not only given attention to higher speed and stronger global optimum search, but also get down to the obstacles and facilitator constraints and practicalities of spatial clustering. Taking into account these constraints during the clustering process is costly and the modeling of the constraints is paramount for good performance. The results on real datasets shown that the Edge detection based spatial clustering with the constraints are performs better than the existing constraint based clustering.
