数据挖掘研究院
Research interests
My primary interests are in algorithms, data mining, computational biology, and ubiquitous computing.
数据挖掘研究院
What′s new?
K. Puolamäki, M. Fortelius, Heikki Mannila: Seriation in Paleontological Data Using Markov Chain Monte Carlo Methods. PLoS Comput Biol 2(2): e6
Jean-Francois Boulicaut, Luc de Raedt, Heikki Mannila (eds.): Constraint-based mining and inductive databases. Springer-Verlag LNCS Volume 3848, ISBN: 3-540-31331-1, Springer 2005.
数据挖掘研究院
J. Seppanen, H. Mannila: Boolean formulas and frequent sets. In Jean-Francois Boulicaut, Luc de Raedt, Heikki Mannila (eds.): Constraint-based mining and inductive databases, Springer-Verlag LNCS Volume 3848, ISBN: 3-540-31331-1, Springer 2005, p. 348-361.
Polish translation of D. Hand, H. Mannila and P. Smyth: Principles of Data Mining available: " Eksploracja danych", Wydawnictwa Naukowo-Techniczne, ISBN 83-204-3053-4, 2005.
F. Afrati, G. Das, A. Gionis, H. Mannila, T. Mielikäinen, P. Tsaparas: Mining chains of relations. ICDM 2005, the Fifth IEEE International Conference on Data Mining, p. 553-556.
数据挖掘研究院
S. Papadimitriou, A. Gionis, P. Tsaparas, R.A. Vaisanen, H. Mannila C. Faloutsos: Parameter-Free Spatial Data Mining Using MDL. ICDM 2005, the Fifth IEEE International Conference on Data Mining, p. 346-353.
数据挖掘研究院
M. Fortelius, A. Gionis, J. Jernvall, H. Mannila, Spectral Ordering and Biochronology of European Fossil Mammals, to appear in Paleobiology.
数据挖掘研究院
P. Rastas, M. Koivisto, H. Mannila, and E. Ukkonen: A hidden Markov technique for haplotype reconstruction. In: R. Casadio and G. Myers (eds.), Algorithms in Bioinformatics: 5th International Workshop, WABI 2005, Lecture Notes in Computer Science, 3692, pp. 140-151, Springer, 2005.
数据挖掘研究院
S. Hyvönen, H. Junninen, L. Laakso, M. Dal Maso, T. Grönholm, B. Bonn, P. Keronen, P. Aalto, V. Hiltunen, T. Pohja, S. Launiainen, P. Hari, H. Mannila, M. Kulmala: A look at aerosol formation using data mining techniques, Atmos. Chem. Phys., 5, 3345-3356, 2005.
数据挖掘实验室
A. Ukkonen, M. Fortelius, H. Mannila: Finding partial orders from unordered 0-1 data. In R. Grossman, R. Bayardo, K. P. Bennett (Eds.): Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, p. 285-293.
数据挖掘研究院
A. Gionis, H. Mannila, P.Tsaparas, Clustering aggregation, In 21st International Conference on Data Engineering (ICDE) 2005. p. 341-352.
数据挖掘实验室
M. Salmenkivi, H. Mannila: Piecewise Constant Modeling of Sequential Data Using Reversible Jump Markov Chain Monte Carlo. In J. Wang, M. Zaki, H. Toivonen, D. Shasha (Eds.): Data Mining in Bioinformatics. Springer 2005, p. 85-103
数据挖掘研究院
M. Salmenkivi, H. Mannila: Using Markov chain Monte Carlo and dynamic programming for event sequence data. Knowl. Inf. Syst. 7(3): 267-288 (2005)
数据挖掘实验室
Mikko Koivisto, Teemu Kivioja, Pasi Rastas, Heikki Mannila, and Esko Ukkonen: Hidden Markov modelling techniques for haplotype analysis. In: S. Ben-David, J. Case, and A. Maruoka (eds.), Algorithmic Learning Theory: 15th International Conference, ALT 2004, Lecture Notes in Computer Science, 3244, pp. 37-52, Springer, 2004.
数据挖掘研究院
F. Geerts, H. Mannila, E. Terzi: Relational link-based ranking . The 30th International Conference on Very Large Data Bases (VLDB′04) , 2004, p. 552-563.
J. Seppänen, H. Mannila, Dense itemsets. In W. Kim, R. Kohavi, J. Gehrke, W. DuMouchel (Eds.): Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2004), p. 683-688.
数据挖掘研究院
A. Gionis, H. Mannila, E. Terzi, Clustered segmentations, 3rd Workshop on Mining Temporal and Sequential Data (TDM) 2004
数据挖掘研究院
A. Gionis, H. Mannila, J. Seppänen, Geometric and combinatorial tiles in 0-1 data, 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) 2004, p. 173-184.
数据挖掘实验室
F. Afrati, A. Gionis, H. Mannila, Approximating a collection of frequent sets, 10th International Conference on Knowledge Discovery and Data Mining (KDD 2004), p. 12-19.
Dmitry Pavlov, H. Mannila, P. Smyth: Beyond independence: probabilistic methods for query approximation on binary transaction data. IEEE Trans. Knowl. Data Eng. 15(6): 1409-1421 (2003)
数据挖掘实验室
Dimitrios Gunopulos, Roni Khardon, Heikki Mannila, Sanjeev Saluja, Hannu Toivonen, and Ram Sewak Sharma. Discovering all most specific sentences. ACM Transactions on Database Systems 28 (2): 140 - 174, June 2003. (DOI: http://doi.acm.org/10.1145/777943.777945)
数据挖掘研究院
Slides of ICDM 2003 invited talk: Global structure from sequences
A. Gionis, T. Kujala and H. Mannila: Fragments of order. ACM SIGKDD 2003, p. 129-136.
数据挖掘研究院
A. Leino, H. Mannila and R.-L. Pitkanen: Rule discovery and probabilistic modeling for onomastic data. PKDD 2003, p. 291-302.
T. Mielikainen and H. Mannila: The Pattern Ordering Problem. PKDD 2003, p. 327-338.
J. Seppanen, E. Bingham and H. Mannila: A simple algorithm for topic identification in 0-1 data. PKDD 2003, p. 423-434.
数据挖掘研究院
A. Gionis and H. Mannila: Finding recurrent sources in sequences. ACM ReCOMB 2003, p. 123-130.
数据挖掘研究院
Y. Zhu, J. Hollmen, R. Raty, Y. Aalto, B. Nagy, E. Elonen, J. Kere, H. Mannila, K. Franssila, S. Knuutila: Investigatory and analytical approaches to differential gene expression profiling in mantle cell lymphoma. Br J Haematol. 2002 Dec;119(4):905-15.
数据挖掘研究院
T. Niini, K. Vettenranta, J. Hollmen, M.L. Larramendy, Y. Aalto, H. Wikman, B. Nagy, J.K. Seppanen, A.F. Salvador, H. Mannila, U.M. Saarinen-Pihkala, S. Knuutila: Expression of myeloid-specific genes in childhood acute lumpoblastic leukemia -- a cDNA array study. Leukemia 16, 2213-2221, 2002.
数据挖掘研究院
Luc de Raedt, Manfred Jaeger, Sau Dan Lee, Heikki Mannila: A theory of inductive query answering. Proceedings of the 2nd IEEE International Conference on Data Mining Vipin Kumar, Shusaku Tsumoto, Ning Zhong, Philip S. Yu, Xindong Wu (Eds.), pp. 123-130, 2002.
数据挖掘研究院
J. Han, R.B. Altman, V. Kumar, H. Mannila, D. Pregibon Emerging Scientific Applications in Data Mining Communications of the ACM 45, 8 (August 2002), 54-58.
M. Salmenkivi, J. Kere, H. Mannila: Genome Segmentation using Piecewise Constant Intensity Models and Reversible Jump MCMC. (European Computational Biology Conference 2002.) Bioinformatics 18, Supplement 2, S211-S218.
数据挖掘研究院
P. Onkamo, V. Ollikainen, P. Sevon, HTT. Toivonen, H. Mannila, and J. Kere: Association analysis for quantitative traits by data mining: QHPM. The Annals of Human Genetics 66 (2002), 419-429.
数据挖掘实验室
Machine Learning: ECML 2002 - 12th European Conference on Machine Learning, LNCS 2430, T. Elomaa, H. Mannila, H. Toivonen (Eds.). Springer 2002.
数据挖掘研究院
Principles of Data Mining and Knowledge Discovery - 6th European Conference, PKDD 2002, LNCS 2431, T. Elomaa, H. Mannila, H. Toivonen (Eds.). Springer 2002.
数据挖掘实验室
E. Bingham, H. Mannila and J. Seppänen: Topics in 0-1 data. To appear in KDD 2002.
H. Mannila: Global and local methods in data mining: basic techniques and open problems. ICALP 2002, 29th International Colloquium on Automata, Languages, and Programming, Malaga, Spain, July 2002; (c) Springer-Verlag
数据挖掘研究院
C.K. Leung, R. Ng, and H. Mannila: OSSM: A Segmentation Approach to Optimize Frequency Counting. ICDE 2002.
数据挖掘研究院
H. Mannila, A. Patrikainen, J. Seppänen, and J. Kere: Long-range control of expression in yeast. Bioinformatics 18, 3 (2002), 482-483.
B. Bollobas, G. Das, D. Gunopulos and H. Mannila: Time-Series Similarity Problems and Well-Separated Geometric Sets. Nordic Journal on Computing, 2001. Shorter version in 13th Annual ACM Symposium on Computational Geometry, 1997, p. 454-456.
Principles of Data Mining , David Hand, Heikki Mannila, and Padhraic Smyth, MIT Press, August 2001.
New links to older papers
Here are links to some papers that previously were unlinked in the full list of publications.
数据挖掘研究院
E. Bingham and H. Mannila: Random projection in dimensionality reduction: applications to image and text data. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2001), F. Provost and R. Srikant (eds.), p. 245-250.
数据挖掘研究院
H. Mannila and C. Meek: Global partial orders from sequential data. Sixth Annual Conference on Knowledge Discovery and Data Mining (KDD-2000), p. 161-168.
数据挖掘研究院
G. Das and H. Mannila: Context-based similarity methods for categorical attributes. Principles of Data Mining and Knowledge Discovery, 4th European Conference (PKDD 2000) D.A. Zighed et al. (eds.), p. 201-211.
数据挖掘研究院
H. Mannila and D. Rusakov: Decomposing event sequences into independent components. First SIAM Conference on Data Mining, 2001.
H. Mannila and J. Seppänen: Recognizing similar situations from event sequences. First SIAM Conference on Data Mining, 2001.
Some links
From Data to Knowledge - Center for Excellence
数据挖掘实验室
Pattern group at HUT (part of From Data to Knowledge) 数据挖掘实验室
Graduate School in Computational biology, bioinformatics, and biometry
数据挖掘研究院
The take-home exam has been graded; contact Heikki Mannila for the results
