Archive

We have a brand new white paper online on the topic Brands need People and People need Brands: How social analytics benefit consumers while protecting their privacy.

Using new social tools, consumers can decide, what information they want to reveal to the advertisers and what to keep private. For marketers and advertisers, this is a unique opportunity. They can concentrate on promoting their products and services to those who have already shown interest to them.

In the white paper, we analyze ways of making marketing relevant and useful for the end users. This can be done by targeting the Alpha users, the most influential persons in a social network.

We list six topics to consider when planning social analytics:

1. Provide simple opt-out
2. Inform users on data usage
3. Protect customer information
4. Respect industry guidelines
5. Let users control their privacy
6. Advertise to youth with care

The white paper also presents, how mobile operators can use Xtract Social Links to find effectively the targets for marketing campaigns. The Product Marketing Module for Xtract Social Links can bring this power to targeting specific products effectively to the influential users.

Did you find the white paper useful? Was there something missing? All feedback is welcome!

Download the white paper here.

We have a new white paper online on the topic Social Advertising Intelligence: How to reach consumers with active advertising.

Customers are increasingly moving from mass broadcast media to social media, making current advertising processes less effective than before. Until now, the tools for planning and buying online and mobile advertising have been lacking.

In the white paper, we analyze the four main challenges that advertisers face with online and mobile advertising, that is:

1. Measuring the full impact of the ads
2. Lack of tools
3. Ads cannot be bought in a unified way
4. Lack of information on online users

The white paper also presents, how Xtract Social Advertising Intelligence can be used to target campaigns effectively, solving some of the challenges with multi-channel advertising.

Did you find the white paper useful? Was there something missing? All feedback is welcome!

Note: The research cited here is property of the respective authors or/and the funding academic institutions.

Sinkkonen J, Aukia J, Kaski S (2007) Inferring vertex properties from topology in large networks. The 5th International Workshop on Mining and Learning with Graphs (MLG’07), Firenze, Italy. link

X. Castelló, R. Toivonen, V.M. Eguíluz, J. Saramäki, K. Kaski, and M. San Miguel, Anomalous lifetime distributions and topological traps in ordering dynamics, Europhysics Letters, in press (2007), arxiv: physics/0705.2560 link

S. Sinha, J. Saramäki and K. Kaski, Phys. Emergence of self-sustained patterns in small-world excitable media, Rev. E 76, 015101(R) (2007), cond-mat/0701121. Selected for the July 15, 2007 issue of Virtual Journal of Biological Physics Research link

J.-P. Onnela, J. Saramäki, J. Hyvönen, G. Szabó, M. Argollo de Menezes, K. Kaski, A.-L. Barabási, and J. Kertész, Analysis of a large-scale weighted network of one-to-one human communication, New Journal of Physics 9, 179 (2007), physics/0702158 link

T. Heimo, J. Saramäki, J.-P. Onnela, and K. Kaski, Spectral and network methods in the analysis of correlation matrices of stock returns, Physica A 383, 147 (2007), physics/0703061 link

J.-P. Onnela, J. Saramäki, J. Hyvönen, G. Szabó, D. Lazer, K. Kaski, J. Kertész, and A.-L. Barabási, Structure and tie strengths in mobile communication networks, Proc. Natl. Acad. Sci. (USA) 104, 7332 (2007), physics/0610104 link

J. Kumpula, J. Saramäki, K. Kaski and J. Kertész, Limited resolution in complex network community detection with Potts model approach, Eur. Phys. J. B 56, 41 (2007), cond-mat/0610370 link

J. Saramäki, M. Kivelä, J.-P. Onnela, K. Kaski and J. Kertész, Generalizations of the clustering coefficient to weighted complex networks, Phys. Rev. E 75, 027105 (2007), cond-mat/0608670. Selected for the March 1, 2007 issue of Virtual Journal of Biological Physics Research link

Matti Aksela, Adaptive combinations of classifiers with application to on-line handwritten character recognition, Doctorate thesis, Helsinki University of Technology, 2007. link

Matti Aksela and Jorma Laaksonen, Adaptive combination of adaptive classifiers for on-line handwritten character recognition, Pattern Recognition Letters, 28(1):136–143, 2007.

J.-P. Onnela, J. Saramäki, J. Hyvonen, G. Szabo, D. Lazer, K. Kaski, J. Kertesz, A.-L. Barabasi. Structure and tie strengths in mobile communication networks Phys. Rev. E 75, 027105 (2007), cond-mat/0608670 link

Note: The research cited here is property of the respective authors or/and the funding academic institutions.

J. Myllykangas, J. Himberg, T. Böhling, B. Nagy, J. Hollmen, and S. Knuutila. DNA copy number amplification of human neoplasms. Oncogene 25:55(7324-7332), 2006.

Heimo, Tapio; Saramäki, Jari; Onnela, Jukka-Pekka; Kaski, Kimmo Spectral and network methods in the analysis of correlation matrices of financial time series. Econophysics Colloquim 2006, Tokio, Japani, 23.-25.11.2006.

Saramäki, Jari; Onnela, Jukka-Pekka; Kertész, J.; Kaski, Kimmo Characterizing Weighted Complex Networks in Economics and Society. Physics of Risk, Vilna, Liettua, 13.-16.5.2006.

Mäkinen, Ville-Petteri; Vehtari, Aki; Salminen, Aino; Saramäki, Jari; Kaski, Kimmo; Ala-Korpela, Mika A. Hierarchical Paradigm for Knowledge Discovery: Towards Biomedical Utilisation of 1H NMR Metabonomics. The 28th Finnish NMR Symposium, Kuopio, Finland, June 7-9, 2006. link

Tibély, Gergely; Onnela, Jukka-Pekka; Saramäki, Jari; Kaski, Kimmo; Kertész, János Spectrum, intensity and coherence in weighted networks of a financial market. Physica A, 2006. Vol. 370, No. 1, pp. 145-150. link

J.-P. Onnela, J. Saramäki, K. Kaski and J. Kertész, Financial market - a network perspective, in Practical Fruits of Econophysics: Nikkei Econophysics III Proceedings, H. Takayasu (Ed.), Springer, Tokyo, 302-306 (2006) link

Toivonen, Riitta; Onnela, Jukka-Pekka; Saramäki, Jari; Hyvönen, Jörkki; Kaski, Kimmo. A Model For Social Networks. Physica A, 2006. Vol. 371, No. 2, pp. 851-860. http://arxiv.org/abs/physics/0601114

Anne Patrikainen, Marina Meila. Comparing Subspace Clusterings. IEEE Transcations on Knowledge and Data Engineering 18(7): 902-916 (2006) link

J. Kallunki, M. Alava and E. K. O. E Hellen. The electric field close to an undulating interface, Journmal of Applied Physics vol 100,023528(2006)

Matti Aksela and Jorma Laaksonen, Using diversity of errors for selecting members of a committee classifier, Pattern Recognition, Vol. 39, Number 4, pp. 608-623, 2006.

J. Särelä, DSS: a flexible framework for source separation, Pattern Analysis and Applications: Bioinformatics or Financial Engineering, 2006.

M. S. C. Almeida, H. Valpola, and J. Särelä, Separation of nonlinear image mixtures by denoising source separation., in Proceedings of the 6th International Conference on Independent Component Analysis and Blind Source Separation (ICA 2006), (Charleston, SC, USA), pp. 8-16, 2006.

J. Martikainen, Methods for improving reliability of evolutionary computation algorithms and accelerating problem solving, Doctoral dissertation, Helsinki University of Technology, Espoo, Finland, 2006.

J. Martikainen and S. J. Ovaska, Comparison of a Fuzzy EP Algorithm and an AIS in Dynamic Optimization Tasks, in Proc. of the IEEE Mountain Workshop on Adaptive and Learning Systems, Logan, UT, 2006, pp. 7-12.

D. Shilane, J. Martikainen, S. Dudoit, and S. J. Ovaska, A general framework for statistical performance comparison of evolutionary computation algorithms, In Proc. of the IASTED International Conference on Artificial Intelligence and Applications, Innsbruck, Austria, 2006, pp. 7-12.

J. Martikainen and S. J. Ovaska, Hierarchical two-population genetic algorithm, International Journal of Computational Intelligence Research, vol. 2, no. 4, pp. 367-381. 2006

Note: The research cited here is property of the respective authors or/and the funding academic institutions.

Kaski S, Sinkkonen J, Klami A: Discriminative clustering. Neurocomputing.

Kaski S, Nikkilä J, Sinkkonen J, Lahti L, Knuuttila J, Roos C: Associative clutering for exploring dependencies between functional genomics data sets. IEEE/ACM Transactions on Computational Biology and Bioinformatics. link

Sysi-Aho, Marko; Saramäki, Jari; Kertesz, J.; Kaski, Kimmo Spatial snowdrift game with myopic agents. European Physical Journal B, 2005. No. 44, pp. 129-135. Electronic publication link

Saramäki, J.; Onnela, J-P.; kertesz, J.; Kaski, K. Characterizing Motifs in Weighted Complex Networks. CNET 2004 - The Science of Complex Networks, Aveiro, Portugali, 29.8.-2.9.2004. New York 2005, American Institute of Physics, pp. 108-117.

Saramäki, Jari; Kaski, Kimmo Modelling the development of epidemics with dynamic small-world networks. Journal of Theoretical Biology, 2005. Vol. 234, pp. 413-421.
Evans, T. S.; Saramäki, J. Scale free networks from self-organisation. Physical Review E, 2005. No. 72, p. 026138. Electronic publication link

Sysi-Aho, Marko; Saramäki, Jari; Kaski, Kimmo. Invisible hand effect in an evolutionary minority game model. Physica A, 2005. Vol. 347, pp. 639-652.

Anne Patrikainen, Marina Meila. Spectral Clustering for Microsoft Netscan Data. Center of Statistics and Social Sciences Working Paper no. 47, University of Washington (2005). link

Matti Aksela and Jorma Laaksonen, On Adaptive Confidences for Critic-Driven Classifier Combining, in Proceedings of ICAPR 2005, pp. 71-80, 2005.

J. Särelä and H. Valpola, Denoising source separation, Journal of Machine Learning Research, vol. 6, pp. 233-272, 2005. link

M. Berg, J.-H. Schleimer, J. Särelä, and T. Honkela, Computing, Philosophy and Cognition, ch. Category learning by formation of regions in conceptual spaces, pp. 381-396. College publications, 2005.

J. Martikainen and S. J. Ovaska, Optimizing dynamical fuzzy systems using aging evolution strategies, in Proc. of the 9th IASTED International Conference on Artificial Intelligence and Soft Computing, Benidorm, Spain, 2005, pp. 5-10.

F. Esposito, T. Scarabino, A. Hyvärinen, J. Himberg, E. Formisano, S. Comani, G. Tedeschi, R. Goebel, E. Seifritz and F. Di Salle. Independent component analysis of fMRI group studies by self-organizing clustering. Neuroimage, 25:1(193-205)

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.

Note: The research cited here is property of the respective authors or/and the funding academic institutions.

M. Rusanen, I.T. Koponen, and J. Kallunki. Mixing length scales: step meandering and island nucleation on vicinal surfaces, European Physical Journal B. vol. 36, p.141 (2003).

Matti Aksela. Comparison of Classifier Selection Methods for Improving Committee Performance, in Proceedings of MCS2003, pp. 84-93, 2003.

Matti Aksela, Ramunas Girdziusas, Jorma Laaksonen, Erkki Oja, Jari Kangas. Methods for adaptive combination of classifiers with application to recognition of handwritten characters, IJDAR Vol. 6 Number 1 (August 2003) pp.23-41, 2003.

J.A. Flanagan, J. Himberg, and J. Mäntyjärvi. A Hierarchical Approach to Learning Context and Facilitating User Interaction in Mobile Devices, in Artificial Intelligence in Mobile System 2003 (AIMS 2003) (in conjunction with Ubicomp 2003, October 12, Seattle, USA.

J. Himberg, J. A. Flanagan, and J. Mäntyjärvi. Towards Context Awareness Using Symbol Clustering Map. In Proc. Workshop for Self-Organizing Maps 2003 (WSOM2003), pp. 249-254, Kitakyushu, Japan, 2003.

J. Himberg, J. Häkkilä, P. Kangas, and J. Mäntyjärvi. On-line personalization of a touch screen based keyboard. In Proceedings of the 2003 International Conference on Intelligent User Interfaces (IUI 2003), pp. 77-84, Miami, Florida, USA, 2003. ACM Press. (download paper from the conference website)

J. Mäntyjärvi, J. Himberg, and P. Huuskonen. Collaborative context recognition for handheld devices. In Proceedings of the First IEEE International Conference on Pervasive Computing and Communications (PerCom 2003), pp. 161-168, Dallas-Fort Worth, Texas, USA, 2003.

Sinkkonen J: Learning Metrics and Discriminative Clustering. PhD thesis. Dissertations on Computer and Information Science, report D2. Espoo, Finland, 2003.

Kaski S, Sinkkonen J, Klami A: Regularized Discriminative Clustering. In C. Molina, T.

Adali, J. Larsen, M. Van Hulle, editors, Neural Networks for Signal Processing XIII, pages 289_298. IEEE, New York, NY, 2003.

Nikkilä J, Roos C, Sinkkonen J, Kaski S: Associative clustering to find dependencies between expression profiles and transcription factor binding. In Proceedings of ECCB2003, European Conference on Computational Biology, Paris, France, September 27-30, pages 433_434. 2003.

Salojärvi J, Kaski S, Sinkkonen J: Discriminative clustering in Fisher metrics. In: Kaynak O, Alpaydin E, Oja E, Xu J (eds.) Arti_cial Neural Networks and Neural Information Processing - Supplementary proceedings ICANN/ICONIP 2003, Istanbul, Turkey, June, pp. 161-164. 2003.

J. Särelä and R. Vigário, Overlearning in marginal distribution-based ICA: analysis and solutions, Journal of Machine Learning Research, vol. 4 (Dec), pp. 1447-1469, 2003. link

Note: The research cited here is property of the respective authors or/and the funding academic institutions.

Vesanto, J. & Hollmén, J. An Automated Report Generation Tool for the Data Understanding Phase. In: Abraham, A., Jain. L. C. & Kacprzyk, J. (eds.), Recent Advances in Intelligent Paradigms and Applications, Studies in Fuzziness and Soft Computing (Vol. 113). Heidelberg 2002, Physica (Springer) Verlag,

Vesanto, J. & Sulkava, M. Distance Matrix Based Clustering of the Self-Organizing Map. International Conference on Artificial Neural Networks - ICANN 2002, Madrid, Spain, August 28-30, 2002. pp. 951-956.

Vesanto, J. & Hollmén, J. An Automated Report Generation Tool for the Data Understanding Phase. First International Conference on Hybrid Intelligent Systems (HIS’01), Adelaide, Australia, Dec. 11-12, 2001. Heidelberg 2002, Physica-Verlag, pp. 611-625.

J. Kallunki, J. Krug and M. Kotrla. Competing mechanisms for step meandering in unstable growth, Physical Review B, vol. 65, 205411 (2002) link

J. Kallunki and J. Krug. Effect of kink-rounding barriers on step edge fluctuations, Surface Science Letters, vol. 523, p. 53 (2002) link

Matti Aksela, Ramunas Girdziusas, Jorma Laaksonen, Erkki Oja, Jari Kangas. Class-Confidence Critic Combining, In Proceedings of IWFHR 8, pp. 201-206, 2002.

J. Mäntyjärvi, P. Huuskonen, and J. Himberg. Collaborative Context Determination to Support Mobile Terminal Applications. IEEE Wireless Communications, 9(5):39-45, 2002.

J. A. Flanagan, J. Mäntyjärvi, and J. Himberg. Unsupervised clustering of symbol strings and context recognition. In Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM 2002), pp. 171-178, Maebashi, Japan, 2002.

Sinkkonen J, Kaski S: Clustering based on conditional distributions in an auxiliary space. Neural Computation, 14:217_239, 2002.

Peltonen J, Sinkkonen J, Kaski S: Discriminative clustering of text documents. In: Wang L, Rajapakse JC, Fukushima K, Lee S-Y, Yao X (eds.) Proceedings of ICONIP’02, 9th International Conference on Neural Information Processing, volume 4, pages 1956_1960. IEEE, Piscataway, NJ. 2002.

Sinkkonen J, Kaski S, Nikkilä J: Discriminative Clustering: Optimal Contingency Tables by Learning Metrics. In: Tapio Elomaa, Heikki Mannila, Hannu Toivonen (eds.) Machine Learning: ECML 2002 (Proceedings of the ECML’02, 13th European Conference on Machine Learning), Lecture Notes in Arti_cial Intelligence 2430, Springer, Berlin, pp. 418_430, 2002.

Note: The research cited here is property of the respective authors or/and the funding academic institutions.

Vesanto, J. Importance of Individual Variables in the k-means Algorithm. 5th Pacific-Area Conference on Knowledge Discovery in Databases (PAKDD2001), Hong Kong, April 16-18, 2001. pp. 513-518.

Matti Aksela, Jorma Laaksonen, Erkki Oja, Jari Kangas. Rejection methods for an adaptive committee classifier, In Proceedings of ICDAR ‘01, pp. 982-986, 2001.

Matti Aksela, Jorma Laaksonen, Erkki Oja, Jari Kangas. Application of adaptive committee classifiers in on-line character recognition In Proceedings of ICAPR’01, pp. 270-279, 2001.

J. Himberg, K. Korpiaho, H. Mannila, J. Tikanmäki, and H.T.T. Toivonen. Time series segmentation for context recognition in mobile devices. In Proceedings of the 2001 IEEE International Conference on Data Mining (ICDM 2001), pp. 203-210, San José, California, USA, 2001.

J. Himberg and A. Hyvärinen. Independent Component Analysis for Binary Data: an Experimental Study. In Proceedings of the 3rd International Workshop on Independent Component Analysis and Blind Signal Separation (ICA2001), pp. 552-556, San Diego, California, USA, 2001.

J. Himberg, J. Mäntyjärvi, and P. Korpipää. Using PCA and ICA for exploratory data analysis in situation awareness. In Proceedings of the 2001 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI 2001), pp. 127-131, Baden-Baden, Germany, 2001.

J. Mäntyjärvi, J. Himberg, and T. Seppänen. Recognizing human motion with multiple acceleration sensors. In Proceedings of the 2001 IEEE International Conference on Systems, Man, and Cybernetics, vol. 2, pp. 747-752, Tucson, Arizona, USA, 2001.

J. Mäntyjärvi, J. Himberg, P. Korpipää, and H. Mannila. Extracting the Context of a Mobile Device User. In Proceedings of the 8th IFAC IFAC/IFIP/IFORS/IEA Symposium on Analysis, Design, and Evaluation of Human-Machine Systems (IFAC HMS 2001), pp. 450-500, Kassel, Germany, 2001.

J. Himberg, J. Ahola, E. Alhoniemi, J. Vesanto, and O. Simula, The Self-Organizing Map as a tool in knowledge engineering. In N. R. Pal, ed., Pattern Recognition in the Soft Computing Paradigm, pp. 38-65. World Scientific Publishing, 2001.

Kaski S, Sinkkonen J, Peltonen J: Bankruptcy analysis with self-organizing maps in learning metrics. IEEE Transactions on Neural Networks, 12:936_947, 2001.

Kaski S, Sinkkonen J, Nikkilä J: Clustering gene expression data by mutual information with gene function. In Georg Dor_ner, Horst Bischof, and Kurt Hornik, editors, Arti_cial Neural Networks - ICANN 2001, pages 81_86. Springer, Berlin, 2001.

Kaski S, Sinkkonen J, Peltonen J: Learning metrics for self-organizing maps. In Proceedings of IJCNN’01, International Joint Conference on Neural Net-works, pages 914_919. IEEE, Piscataway, NJ, 2001.

Kaski S, Sinkkonen J: A topography-preserving latent variable model with learning metrics. In N. Allinson, H. Yin, L. Allinson, and J. Slack, editors, Advances in Self-Organizing Maps, pages 224_229. Springer, London, 2001.

Nikkilä J, Törönen P, Sinkkonen J, Kaski S: Analysis of gene expression data using semi-supervised clustering. In Proceedings of Bioinformatics 2001, Skövde, Sweden, March 29-April 1, pages 2_3. 2001.

Note: The research cited here is property of the respective authors or/and the funding academic institutions.

Vesanto, J., Himberg, J. & Alhoniemi, E. SOM Toolbox for Matlab 5. Espoo, Finland: 2000. (Helsinki University of Technology, Publications in Computer and Information Science Report A57).

Vesanto, J.Neural Network Tool for Data Mining: SOM Toolbox. 5th International Symposium on Tool Environments and Development Methods for Intelligent Systems (TOOLMET2000), Oulu, Finland, April 13-14, 2000. Oulu, Finland 2000, Oulun yliopistopaino, pp. 184-196.

Vesanto, J. & Alhoniemi, E. Clustering of the Self-Organizing Map. IEEE Transactions on Neural Networks, 2000. Vol. 11 , No. 3, pp. 586-600.

J. Kallunki, M. Dube and T. Ala-Nissilla: Adatom dynamics in a periodic potential under a time-periodic bias, Surface Science, vol. 460, p. 39 (2000)

J. Kallunki and J. Krug. Asymptotic step profiles from a nonlinear growth equation for vicinal surfaces, Physical Review E, vol. 62, p. 6229 (2000) link

Vuokko Vuori, Matti Aksela, Jorma Laaksonen, Erkki Oja, Jari Kangas. Adaptive Character Recognizer for a Hand-held device: Implementation and Evaluation Setup. In Proceedings of IWFHR 7, pp 13-22, 2000.

J. Himberg. A SOM based cluster visualization and its application for false coloring. In Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN 2000), vol. 3, pp. 587-592, Como, Italy, 2000.

J. Vesanto, J. Himberg, E. Alhoniemi, and J. Parhankangas, SOM Toolbox for Matlab 5, Technical Report A57, Helsinki University of Technology, Neural Networks Research Centre, 2000.

Kaski S, Sinkkonen J: Metrics that learn relevance. In Proceedings of IJCNN-2000, International Joint Conference on Neural Networks, volume V, pages 547_552. IEEE Service Center, Piscataway, NJ, 2000.

Sinkkonen J, Kaski S: Clustering by similarity in an auxiliary space. In Proceedings of IDEAL 2000, Second International Conference on Intelligent Data Engineering and Automated Learning. Springer, 2000

Sinkkonen J: Information and resource allocation. In: Baddeley R, Hancock P, Földiák P (eds.) Information theory and the brain. 2000. Cambridge University Press.

R. Vigário, J. Särelä, and E. Oja, Advances in Independent Component Analysis, ch. Independent Component Approach to the Analysis of EEG and MEG Recordings. Springer, Berlin, 2000.