A Quaternion Clustering Framework

Abhayasinghe, N. and Murray, I. (2014). Human gait phase recognition based on thigh movement computed using imus, 9th IEEE International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), Singapore, pp. 1–4.Search in Google Scholar

Austin, S.B., Melly, S.J., Sanchez, B.N., Patel, A., Buka, S. and Gortmaker, S.L. (2005). Clustering of fast-food restaurants around schools: A novel application of spatial statistics to the study of food environments, American Journal of Public Health95(9): 1575–1581.10.2105/AJPH.2004.056341Search in Google Scholar

Bandyopadhyay, S. and Maulik, U. (2002). Genetic clustering for automatic evolution of clusters and application to image classification, Pattern Recognition35(6): 1197–1208.10.1016/S0031-3203(01)00108-XSearch in Google Scholar

Berkhin, P. (2006). A survey of clustering data mining techniques, in J. Kagan et al. (Eds), Grouping Multidimensional Data, Springer, Berlin/Heidelberg, pp. 25–71.10.1007/3-540-28349-8_2Search in Google Scholar

Caliński, T. and Harabasz, J. (1974). A dendrite method for cluster analysis, Communications in Statistics3(1): 1–27, DOI: 10.1080/03610927408827101.10.1080/03610927408827101Search in Google Scholar

Cantador, I. and Castells, P. (2006). Multilayered semantic social network modeling by ontology-based user profiles clustering: Application to collaborative filtering, in S. Staab and V. Svwtek (Eds), Managing Knowledge in a Word of Networks, Springer, Berlin/Heidelberg, pp. 334–349.10.1007/11891451_30Search in Google Scholar

Chaturvedi, N.A., Sanyal, A.K. and McClamroch, N.H. (2011). Rigid-body attitude control, IEEE Control Systems31(3): 30–51.10.1109/MCS.2011.940459Search in Google Scholar

Creusot, C., Pears, N. and Austin, J. (2010). 3D face landmark labelling, Proceedings of the ACM Workshop on 3D Object Retrieval, Firenze, Italy, pp. 27–32.Search in Google Scholar

Davies, D.L. and Bouldin, D.W. (1979). A cluster separation measure, IEEE Transactions on Pattern Analysis and Machine Intelligence1(2): 224–227, DOI: 10.1109/TPAMI.1979.4766909.10.1109/TPAMI.1979.4766909Search in Google Scholar

Dunn, J.C. (1974). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters, Journal of Cybernetics3(3): 32–57.10.1080/01969727308546046Search in Google Scholar

Ell, T.A. and Sangwine, S.J. (2007). Hypercomplex Fourier transforms of color images, IEEE Transactions on Image Processing16(1): 22–35.10.1109/TIP.2006.884955Search in Google Scholar

Gariel, M., Srivastava, A.N. and Feron, E. (2011). Trajectory clustering and an application to airspace monitoring, IEEE Transactions on Intelligent Transportation Systems12(4): 1511–1524.10.1109/TITS.2011.2160628Search in Google Scholar

Grubesic, T.H. (2006). On the application of fuzzy clustering for crime hot spot detection, Journal of Quantitative Criminology22(1): 77–105.10.1007/s10940-005-9003-6Search in Google Scholar

Han, J., Pei, J. and Kamber, M. (2011). Data Mining: Concepts and Techniques, Elsevier, Waltham, MA.Search in Google Scholar

Himberg, J., Hyvärinen, A. and Esposito, F. (2004). Validating the independent components of neuroimaging time series via clustering and visualization, Neuroimage22(3): 1214–1222.10.1016/j.neuroimage.2004.03.02715219593Search in Google Scholar

Huynh, D.Q. (2009). Metrics for 3D rotations: Comparison and analysis, Journal of Mathematical Imaging and Vision35(2): 155–164, DOI: 10.1007/s10851-009-0161-2.10.1007/s10851-009-0161-2Search in Google Scholar

Jabłoński, B. (2008a). Anisotropic filtering of multidimensional rotational trajectories as a generalization of 2D diffusion process, Multidimensional Systems and Signal Processing19(3): 379–399, DOI: 10.1007/s11045-008-0056-1.10.1007/s11045-008-0056-1Search in Google Scholar

Jabłoński, B. (2008b). Filtration of Images and Spatial Trajectories Using Partial Differential Equations,EXIT,Warsaw, (in Polish).Search in Google Scholar

Jabłoński, B. (2011). Application of quaternion scale space approach for motion processing, in R.S. Choraś (Ed.), Image Processing and Communications Challenges 3, Springer, Berlin/Heidelberg, pp. 141–148, DOI: 10.1007/978-3-642-23154-4_16.10.1007/978-3-642-23154-4_16Search in Google Scholar

Jabłoński, B. (2012). Quaternion dynamic time warping, IEEE Transactions on Signal Processing60(3): 1174–1183.10.1109/TSP.2011.2177832Search in Google Scholar

Johnson, M. (2003). Exploiting Quaternions to Support Expressive Interactive Character Motion, PhD thesis, MIT, Cambridge, MA.Search in Google Scholar

Koster, K. and Spann, M. (2000). MIR: An approach to robust clustering-application to range image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence22(5): 430–444.10.1109/34.857001Search in Google Scholar

Liao, T.W. (2005). Clustering of time series data—a survey, Pattern Recognition38(11): 1857–1874.10.1016/j.patcog.2005.01.025Search in Google Scholar

Loots, M.T., Bekker, A., Arashi, M. and Roux, J.J. (2013). On the real representation of quaternion random variables, Statistics47(6): 1224–1240, DOI: 10.1080/02331888.2012.695376.10.1080/02331888.2012.695376Search in Google Scholar

Markley, F.L., Cheng, Y., Crassidis, J.L. and Oshman, Y. (2007). Averaging quaternions, Journal of Guidance, Control, and Dynamics30(4): 1193–1197.10.2514/1.28949Search in Google Scholar

Maulik, U. and Bandyopadhyay, S. (2002). Performance evaluation of some clustering algorithms and validity indices, IEEE Transactions on Pattern Analysis and Machine Intelligence24(12): 1650–1654.10.1109/TPAMI.2002.1114856Search in Google Scholar

Pei, S.-C. and Cheng, C.-M. (1999). Color image processing by using binary quaternion-moment-preserving thresholding technique, IEEE Transactions on Image Processing8(5): 614–628.10.1109/83.76031018267478Search in Google Scholar

Piórek, M. (2018). Analysis of Chaotic Behavior in Non-linear Dynamical Systems, Springer, Cham.10.1007/978-3-319-94887-4Search in Google Scholar

Reumerman, H.-J., Roggero, M. and Ruffini, M. (2005). The application-based clustering concept and requirements for intervehicle networks, IEEE Communications Magazine43(4): 108–113.10.1109/MCOM.2005.1421913Search in Google Scholar

Risojević, V. and Babić, Z. (2013). Unsupervised learning of quaternion features for image classification, 11th International Conference on Telecommunication in Modern Satellite, Cable and Broadcasting Services (TELSIKS), Serbia, Nis, Vol. 1, pp. 345–348.Search in Google Scholar

Shi, L. (2005). Exploration in Quaternion Colour, PhD thesis, Simon Fraser University, Burnaby.Search in Google Scholar

Shi, L. and Funt, B. (2007). Quaternion color texture segmentation, Computer Vision and Image Understanding107(1): 88–96.10.1016/j.cviu.2006.11.014Search in Google Scholar

Wu, Z. and Leahy, R. (1993). An optimal graph theoretic approach to data clustering: Theory and its application to image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence15(11): 1101–1113.10.1109/34.244673Search in Google Scholar

Zhou, F., De la Torre, F. and Hodgins, J.K. (2008). Aligned cluster analysis for temporal segmentation of human motion, 8th IEEE International Conference on Automatic Face & Gesture Recognition, FG’08, Amsterdam, The Netherlands, pp. 1–7.Search in Google Scholar