Note: The research cited here is property of the respective authors or/and the funding academic institutions.
J. Himberg. From Insights to Innovations: Data Mining, Visualization, and User Interfaces Report D5 in series Dissertations in Computer and Information Science, Helsinki University of Technology.
Vesanto, J. & Hollmén, J. An Automated Report Generation Tool for the Data Understanding Phase. In: Abraham, A. , Jain, L. & van der Zwaag, B. J.(eds.), Innovations in Intelligent Systems: Design, Management and Applications, Studies in Fuzziness and Soft Computing Vol. 140. Heidelberg 2004, Springer (Physica) Verlag, chapter 5.
Kaski, Kimmo; Saramäki, Jari. Modelling Epidemics With Dynamic Small-World Networks. CNET 2004 - The Science of Complex Networks, Aveiro, Portugali 29.8.-2.9.2004. New York 2005, American Institute of Physics, pp. 252-262.
Anne Patrikainen, Heikki Mannila. Subspace Clustering of High-Dimensional Binary Data - A Probabilistic Approach. Proc. of the 4th SIAM International Conference on Data Mining, Workshop on Clustering High Dimensional Data (2004).
J. Kallunki and J. Krug. Breakdown of step-flow growth in unstable homoepitaxy, Europhysics Letters, vol. 66, p. 749 (2004) link
J. Mäntyjärvi, K. Nybergh, J. Himberg, and K. Hjelt. Touch Detection System for Mobile Terminals, In Proc. MobileHCI’04. (short paper). In series Lecture Notes in Computer Science, vol. 3160. Springer-Verlag.
J. Mäntyjärvi, J. Himberg, P. Kangas, U. Tuomela, and P. Huuskonen. Sensor Signal Data Set for Exploring Context Recognition of Mobile Devices In Workshop “Benchmarks and a database for context recognition” in conjuction with the 2nd Int. Conf. on Pervasive Computing (PERVASIVE 2004), April 18-23, Linz/Vienna, Austria, 2004.
Kaski S, Sinkkonen J: Principle of learning metrics for data analysis. The Journal of VLSI Signal Processing-Systems for Signal, Image, and Video Technology, special issue on Data Mining and Biomedical Applications of Neu-ral Networks. 37:177_188, 2004.
Peltonen J, Sinkkonen J, Kaski S: Sequential Information Bottleneck for Finite Data. In: Greiner R, Schuurmans D (eds.) Proceedings of the Twenty-First Internaltional Conference on Machine Learning (ICML 2004). Omnipress, Madison, WI. pp. 637_654. 2004.
Sinkkonen J, Nikkilä J, Lahti L, Kaski S: Associative Clustering. In: Boulicaut, Esposito, Giannotti, Pedreschi (eds.) Machine Learning: ECML2004 (Proceddings of the 15th European Conference on Machine Learning). Lecture Notes in Computer Science 3201, pp. 396_406. 2004.
H. Valpola and J. Särelä, Accurate, fast and stable denoising source separation algorithms, in Proceedings of the Fifth International Con- ference on Independent Component Analysis and Signal Separation (ICA2004), (Granada, Spain), pp. 64-71, 2004.
J. Raitio, R. Vigario, J. Särelä, and T. Honkela, Assessing similarity of emergent representations based on unsupervised learning, in International Joint Conference on Neural Networks (IJCNN2004), (Budapest, Hungary), pp. I.597-I.602, 2004.
Saramäki, Jari, A Method for Decentralized Optimisation in Networks. CNET 2004 - The Science of Complex Networks, Aveiro, Portugali, 29.8.-2.9.2004. New York 2005, American Institute of Physics, pp. 215-223.
J. Himberg, A. Hyvärinen, and F. Esposito. Validating the independent components of neuroimaging time-series via clustering and visualization. Neuroimage, 22:3(1214-1222), 2004.
Karp, E.; Gävert, H.; Särelä, J.; Vigário, R. Independent Component Analysis Decomposition of Structural MRI. 2nd IASTED International Conference on Biomedical Engineering (BioMED 04), Innsbruck, Austria, Feb. 16-18, 2004. pp. 83-87.
Hugo Gävert. Bankruptcy Prediction and Cluster Analysis of Small and Medium-Sized Enterprises Based on Financial Statements. Master’s Thesis, Helsinki University of Technology, Espoo, Finland, 2004.
