Search Results

1 - 10 of 2,876 items :

Clear All
Linguistically Defined Clustering of Data

References Chang, H. and Yeung, D. (2008). Robust path-based spectral clustering, Pattern Recognition 41(1): 191-203. Comaniciu, D. and Meer, P. (2002). Mean shift: A robust approach toward feature space analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5): 603-619. Duda, R., Hart, P. and Stork, D. (2001). Pattern Classification, Wiley, New York, NY. Ester, M., Kriegiel, H.-P., Sander, J. and Xu, X. (1996). A density-based algorithm for discovering clusters in large

Open access
Different Approaches to Clustering – Cassini Ovals

References [1] B. S. Everitt, Cluster analysis. London: Edward Arnold, 1993. [2] J. D. Lawrence, A Catalog of Special Plane Curves. New York: Dover, 1972. [3] E. H. Lockwood, A Book of Curves. Cambridge, England: Cambridge University Press, 1967. [4] V. Rovenski, Modeling of Curves and Surfaces with MATLAB. Springer, 2010. [5] M. I. Skolnik, Introduction to Radar Systems. McGraw-Hill, 2001. [6] D. E. Smith, History of Mathematics. Vol. II., New York: Dover, 1958

Open access
Genetic Algorithm Based Clustering for Large-Scale Sensor Networks

References 1. Younis, M. Krunz, S. Ramasubramanian. Node Clustering in Wireless Sensor Networks: Recent Developments and Deployment Challenges. – IEEE Network, Vol. 20 , May-June 2006, No 3, pp. 20-25. 2. Al-Karaki, J. N., A. E. Kamal. Routing Techniques in Wireless Sensor Networks: A Survey. – IEEE Wireless Commun., Vol. 11 , December 2004, No 6, pp. 6-28. 3. Heinzelman, W. B., A. P. Chandrakasan, H. Balakrishnan. An Application-Specific Protocol Architecture for Wireless Microsensor Networks. – IEEE Trans. Wireless Commun., Vol. 1 , October

Open access
Heuristic possibilistic clustering for detecting optimal number of elements in fuzzy clusters

References [1] Anderson E., The irises of the Gaspe Peninsula, Bulletin of the American Iris Society , 59 , 1, 1935, 2-5. [2] Bezdek J.C., Pattern Recognition with Fuzzy Objective Function Algorithms , Plenum Press, New York, 1981. [3] Chiang J.-H., Yue S., Yin Z.-X., A new fuzzy cover approach to clustering, IEEE Transactions on Fuzzy Systems , 12 , 2, 2004, 199-208. [4] Corsini P., Lazzerini B., Marcelloni F., A new fuzzy relational clustering algorithm based on the fuzzy C-means algorithm, Soft Computing , 9 , 6, 2005, 439

Open access
Clustering Methodology for Time Series Mining

. - World Scientific Publishing , 2004. Vol.57, pp. 67-100. Agrawal R., Faloutsos C., Swami A. Efficient similarity search in sequence databases. Proc. 4 th Int. Conf. On Foundations of Data Organizations and Algorithms, 1993. - Chicago. pp. 69-84. Faloutsos C., Ranganathan M., Manolopoulos Y. Fast subsequence matching in time-series databases. Proc. ACM SIGMOID Int. Conf. on Management of Data, 1994. - Minneapolis. pp. 419 - 429. Keogh E., Lin J., Truppel W. Clustering of time series

Open access
Distance Metrics Selection Validity in Cluster Analysis

References S. Jahirabadkar, P. Kulkarni, ISC- Intelligent Subspace Clustering, A Density Based Clustering Approach for High Dimensional dataset , World Academy of Science, Engineering and Technology, 55, 2009. J. Han M. Kamber, and A. K. H. Tung. Geographic Data Mining and Knowledge Discovery , chapter Spatial Clustering Methods in Data Mining: A Survey, pages 1-29. Taylor and Francis, 2001. B. S. Everitt, Cluster analysis. Edward Arnold, London, 1993

Open access
Multiple Manifolds Clustering via Local Linear Analysis

References 1. Duda, R. O., P. E. Hart, D. G. Stork. Pattern Classification. 2nd ed. New York, Wiley, 2000. 2. Vida, R. Subspace Clustering. – IEEE Signal Processing Magazine, Vol. 28 , 2011, No 2, pp. 52-68. 3. Shi, J., J. Malik. Normalized Cuts and Image Segmentation. – IEEE Transactions Pattern Analysis Machine Intelligence, Vol. 22 , 2000, No 2, pp. 888-905. 4. Liu, G., et al. Robust Recovery of Subspace Structures by Low-Rank Representation. – IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 68 , 2013, No 1, pp

Open access
Descriptor Fingerprints and Their Application to WhiteWine Clustering and Discrimination.

://repositorium.sdum.uminho.pt/bitstream/1822/10029/1/wine5.pdf [4]. Bangov, I.; Moskovkina, M.; Stojanov, B.; Descriptor Fingerprints and Their Application to Red Wine Clustering and Discrimination, Acta Scientifica Naturalis, 2017 , 1, 29-34. [5]. Butina, D., Unsupervised data base clustering based on Daylight’s fingerprint and Tanimoto similarity: A fast and automated way to cluster small and large data sets, J. Chem. Inf. Comput. Scie., 1999. 39, 747 - 750.

Open access
Clustering Macroeconomic Time Series

Bibliography Ahlborn M., Wortmann M., 2018, The core–periphery pattern of European business cycles: a fuzzy clustering approach , Journal of Macroeconomics, 55, pp. 12–27. Retrieved from http://hdl.handle.net/10419/152248 . Belke A., Domnick C., Gros D., 2017, Business Cycle Synchronization in the EMU: Core vs. Periphery (Working Paper No. 38), GLO Discussion Paper. Retrieved from http://hdl.handle.net/10419/156158 . Croux C., Forni M., Reichlin L., 2001, A measure of comovement for economic variables: theory and empirics , The Review of

Open access
Center-based l1–clustering method

References Angulo, J. and Serra, J. (2007). Modelling and segmentation of colour images in polar representations, Image and Vision Computing 25 (4): 475-495. Äyrämö, S. (2006). Knowledge Mining Using Robust Clustering , Ph.D. thesis, University of Jyväskylä, Jyväskylä. Bagirov, A.M. and Ugon, J. (2005). An algorithm for minimizing clustering functions, Optimization 54 (4-5): 351-368. Bagirov, A.M., Ugon, J. and Webb, D. (2011). Fast modified global k -means algorithm for incremental cluster

Open access