Selected publications of Heikki Mannila

Last updated May 2008

  1. H. Mannila and K.-J. Räihä: Design of Relational Databases. Addison-Wesley Publishing Company 1992; ISBN 0-201-56523-4. Reprinted 1994.

  2. D. Hand, H. Mannila and P. Smyth: Principles of Data Mining. MIT Press 2001. ISBN 1-57735-027-8. Reprinted 2002. Second Printing, 2002. Chinese translation, China Machine Press, ISBN 7-111-11577-5, 2003. Polish translation "Eksploracja danych", Wydawnictwa Naukowo-Techniczne, ISBN 83-204-3053-4, 2005.

    Edited books

  3. David Heckerman, H. Mannila, Daryl Pregibon (eds): Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), AAAI Press 1997; ISBN 1-57735-027-8.

  4. Robert Grossman, Jiawei Han, Vipin Kumar, Heikki Mannila, and Rajeev Motwani (eds.) Proceedings of the Second SIAM International Conference on Data Mining. SIAM 2002; ISBN 0-89871-517-2

  5. T. Elomaa, H. Mannila, H. Toivonen (eds.), Machine Learning: ECML 2002 - 12th European Conference on Machine Learning, LNCS 2430, Springer 2002.

  6. T. Elomaa, H. Mannila, H. Toivonen (eds.), Principles of Data Mining and Knowledge Discovery - 6th European Conference, PKDD 2002, LNCS 2431, Springer 2002.

  7. 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.

    Articles in international journals and refereed international conferences

    2009--

  8. J. Wessman, T. Paunio, A. Tuulio-Henriksson, M. Koivisto, T. Partonen, J. Suvisaari, JA. Turunen, J. Wedenoja, W. Hennah, O. Pietilainen, J. Lonnqvist, H. Mannila, L. Peltonen: Mixture model clustering of phenotype features reveals evidence for association of DTNBP1 to a specific subtype of schizophrenia. Biological Psychiatry, Volume 66, Issue 11, Pages 990-996, 2009.

  9. T. Vesala, S. Launiainen, P. Kolari, J. Pumpanen, S. Sevanto, P. Hari, E. Nikinmaa, P. Kaski, H. Mannila, E. Ukkonen, S. Piao and P. Ciais: Autumn temperature and carbon balance of a boreal Scots pine forest in Southern Finland. Biogeosciences (accepted).

  10. M. Ojala, G. Garriga, A. Gionis, H. Mannila: Evaluating Query Result Significance in Databases via Randomizations. To appear in SDM'10: Proceedings of the 2010 SIAM International Conference on Data Mining

  11. M. Miah, G. Das, V. Hristidis, H. Mannila: Determining Attributes to Maximize Visibility of Objects IEEE Transactions on Knowledge and Data Engineering 21, 7 (2009), 959-973.

  12. T. Feder, H. Mannila, E. Terzi: Approximating the Minimum Chain Completion problem . Information Processing Letters, 109, 17, 2009, 980-985.

  13. S. Hanhijärvi, M. Ojala, N. Vuokko, K. Puolamäki, N. Tatti, and H. Mannila: Tell me something I don't know: Randomization strategies for iterative data mining. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '09), to appear.

  14. L.H. Liow, M. Fortelius, K. Lintulaakso, H. Mannila, N.Chr. Stenseth: Lower Extinction Risk in Sleep-or-Hide Mammals. American Naturalist 2009. Vol. 173, pp. 264-272.

  15. A. Ukkonen, K. Puolamäki, A. Gionis, H. Mannila: A Randomized Approximation Algorithm for Computing Bucket Orders. Information Processing Letters 109(7):356-359, 2009.

    2008

  16. H. Mannila: Finding Total and Partial Orders from Data for Seriation, Discovery Science 2008 p. 16-25.

  17. G. Garriga, A. Ukkonen, H. Mannila: Feature Selection in Taxonomies with Applications to Paleontology, Discovery Science 2008 p. 112--123. [Correction.]

  18. N. Haiminen, H. Mannila, E. Terzi: Determining significance of pairwise co-occurrences of events in bursty sequences. BMC Bioinformatics 9(336), 2008. [online, open access]

  19. P. Miettinen, T. Mielikainen, A. Gionis, G. Das, H. Mannila: The Discrete Basis Problem. To appear in IEEE Transactions on Knowledge and Data Engineering, 20(10), October 2008. [PrePrint from IEEE] (An expanded versio of P. Miettinen, T. Mielikainen, A. Gionis, G. Das, H. Mannila: The Discrete Basis Problem. 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) 2006, p. 335-346. PKDD Best Paper.

  20. N. Haiminen, H. Mannila: Evaluation of BIC and cross validation for model selection on sequence segmentations. International Journal of Data Mining and Bioinformatics (IJDMB) (in press).

  21. L.H. Liow, M. Fortelius, E. Bingham, K. Lintulaakso, H. Mannila, L. Flynn, and N.Chr. Stenseth Higher origination and extinction rates in larger mammals. Proc Natl Acad Sci 105(16), pp. 6097-6102, 2008.

  22. G. Garriga, E. Junttila, H. Mannila: Banded structure in binary matrices. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2008), Las Vegas, Nevada, United States, August 24-27th, 2008, pages 292-300.

  23. P.E. Lundmark, U. Liljedahl, D.I. Boomsma, H. Mannila, N.G. Martin, A. Palotie, L. Peltonen, M. Perola, T.D. Spector and A.-C. Syvänen: Evaluation of HapMap data in six populations of European descent. European Journal of Human Genetics 2008, 1-9.

  24. P. Rastas, M. Koivisto, H. Mannila, and E. Ukkonen: Phasing genotypes using a hidden Markov model. In: Bioinformatics Algorithms: Techniques and Applications, I. Mandoiu and A. Zelikovsky (eds.), p. 373-391, Wiley 2008.

  25. P. Miettinen, A. Gallo, H. Mannila: Finding duplicate descriptors: algorithms for redescription mining. SIAM Data Mining Conference 2008, p. 334-345.

  26. M. Ojala, N. Vuokko, A. Kallio, N. Haiminen, H. Mannila: Randomization of real-valued matrices for assessing the significance of data mining results. SIAM Data Mining Conference 2008, p. 494-505..

  27. B. Goethals, W. Le Page, and Heikki Mannila, Mining Association Rules of Simple Conjunctive Queries, SIAM Data Mining Conference 2008, p. 96-107.

  28. M. Miah, V. Hristidis, G. Das, H. Mannila: Standing Out in a Crowd: Selecting Attributes for Maximum Visibility. International Conference on Data Engineering (ICDE 2008), p. 356-365.

    2007

  29. A. Gionis, H. Mannila, T. Mielikainen, and P. Tsaparas, Assessing Data Mining Results via Swap Randomization, ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 1 , Issue 3 (December 2007) Article No. 14.

  30. H. Mannila: The role of information technology for systems biology. In Systems Biology: A Grand Challenge for Europe, ESF 2007, p. 21-23.

  31. A. Ukkonen and H. Mannila: Finding Outlying Items in Sets of Partial Rankings . In: Knowledge Discovery in Databases: PKDD 2007, p. 265-276.

  32. S. Hyvonen, A. Gionis, and H. Mannila: Recurrent predictive models for sequence segmentation. Advances in Intelligent Data Analysis VII (IDA 2007), p. 195-206.

  33. H. Mannila and E. Terzi: Nestedness and segmented nestedness. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2007), p. 480-489.

  34. H. Heikinheimo, E. Hinkkanen, H. Mannila, T. Mielikäinen, and J. Seppänen, Finding low-entropy ssets and trees from binary data In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (KDD 2007), p. 350-359.

  35. Niina Haiminen, Heikki Mannila, Evimaria Terzi: Comparing segmentations by applying randomization techniques. BMC Bioinformatics, accepted for publication 2007.

  36. N. Haiminen, H. Mannila: Discovering isochores by least-squares optimal segmentation. Gene 394 (Issues 1-2), 2007, pp. 53-60 (1 June 2007). [online via ScienceDirect]

  37. N. Landwehr, T. Mielikäinen, L. Eronen, H. Toivonen and H. Mannila, Constrained hidden Markov models for population-based haplotyping, BMC Bioinformatics 2007, 8(Suppl 2):S9.

  38. A. Dasgupta, G. Das, and H. Mannila: A Random Walk Approach to Sampling Hidden Databases. SIGMOD 2007, to appear.

  39. A. Hinneburg, H. Mannila, S. Kaislaniemi, T. Nevalainen and H. Raumolin-Brunberg: How to Handle Small Samples: Bootstrap and Bayesian Methods in the Analysis of Linguistic Changea, Literary and Linguistic Computing; doi: 10.1093/llc/fqm006

  40. H. Heikinheimo, M. Fortelius, J. Eronen and H. Mannila: Biogeography of European land mammals shows environmentally distinct and spatially coherent clusters. Journal of Biogeography 34, 6, 1053-1064 (2007). doi:10.1111/j.1365-2699.2006.01664.x

  41. A. Gionis, H. Mannila, P. Tsaparas: Clustering Aggregation (long version) ACM Transactions on Knowledge Discovery from Data, 1, 1 (2007),

    2006

  42. R. Gwadera, A. Gionis, and H. Mannila, Optimal Segmentation using Tree Models. 2006 IEEE International Conference on Data Mining, p. 244-253, 2006

  43. N. Tatti, T. Mielikainen, A. Gionis, and H. Mannila, What is the dimension of your binary data? 2006 IEEE International Conference on Data Mining, p. 603-612, 2006.

  44. P. Miettinen, T. Mielikäinen, A. Gionis, G. Das, H. Mannila: The Discrete Basis Problem. 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) 2006, p. 335-346. PKDD Best Paper.

  45. H. Heikinheimo, H. Mannila, J. Seppänen: Finding Trees from Unordered 0-1 Data. 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) 2006, p. 175-186.

  46. A. Gionis, H. Mannila, T. Mielikainen, and P. Tsaparas, Assessing Data Mining Results via Swap Randomization, 12th International Conference on Knowledge Discovery and Data Mining (KDD) 2006, p. 167-176. Runner-up, KDD Best Paper award.

  47. A. Gionis, H. Mannila, K. Puolamaki, and A. Ukkonen, Algorithms for Discovering Bucket Orders from Data, 12th International Conference on Knowledge Discovery and Data Mining (KDD) 2006, p. 561-566.

  48. N. Landwehr, T. Mielikainen, L. Eronen, H. Toivonen, and H. Mannila: Constrained Hidden Markov Models for Population-based Haplotyping, PMSB 2006, to appear.

  49. K. Puolamäki, M. Fortelius, H. Mannila: Seriation in Paleontological Data Using Markov Chain Monte Carlo Methods. PLoS Comput Biol 2(2): e6

  50. E. Bingham, A. Gionis, N. Haiminen, H. Hiisila, H. Mannila, E. Terzi: Segmentation and Dimensionality Reduction, SIAM Data Mining Conference (SDM) 2006.

    2005

  51. 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.

  52. 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.

  53. 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.

  54. 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.

  55. M. Fortelius, A. Gionis, J. Jernvall, H. Mannila, Spectral Ordering and Biochronology of European Fossil Mammals, Paleobiology 32, 2, 206-214.

  56. 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.

  57. 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.

  58. 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.

  59. A. Gionis, H. Mannila, P.Tsaparas, Clustering aggregation, In 21st International Conference on Data Engineering (ICDE) 2005. p. 341-352.

  60. 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

  61. M. Salmenkivi, H. Mannila: Using Markov chain Monte Carlo and dynamic programming for event sequence data. Knowl. Inf. Syst. 7(3): 267-288 (2005)

    2004

  62. A. Patrikainen, H. Mannila: Subspace clustering of high-dimensional binary data - A probabilistic approach. Workshop on Clustering High-Dimensional Data and Its Applications, SIAM International Conference on Data Mining 2004, pp. 57-65.

  63. 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.

  64. 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.

  65. 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.

  66. A. Gionis, H. Mannila, E. Terzi, Clustered segmentations, 3rd Workshop on Mining Temporal and Sequential Data (TDM) 2004

  67. 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.

  68. 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.

    69. 2003

  69. 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)

  70. 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)

  71. A. Gionis, T. Kujala and H. Mannila: Fragments of order. ACM SIGKDD 2003, p. 129-136.

  72. A. Leino, H. Mannila and R.-L. Pitkanen: Rule discovery and probabilistic modeling for onomastic data. PKDD 2003, p. 291-302.

  73. T. Mielikainen and H. Mannila: The Pattern Ordering Problem. PKDD 2003, p. 327-338.

  74. J. Seppanen, E. Bingham and H. Mannila: A simple algorithm for topic identification in 0-1 data. PKDD 2003, p. 423-434.

  75. A. Gionis and H. Mannila: Finding recurrent sources in sequences. ACM ReCOMB 2003, to appear.

    2002

  76. 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.

  77. 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.

  78. 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.

  79. Luc de Raedt, Manfred Jaeger, Sau Dan Lee, Heikki Mannila: A Theory of Inductive Query Answering. In Proceedings of ICDM'02 (to appear).

  80. M. Koivisto, M. Perola, T. Varilo, W. Hennah, J. Ekelund, M. Lukk, L. Peltonen, E. Ukkonen, H. Mannila: An MDL method for finding haplotype blocks and for estimating the strength of haplotype block boundaries. Pacific Symposium on Biocomputing 2003, R.B. Altman, A.K. Dukner, L. Hunter T.A. Jung and T.E. Klein, eds., World Scientific 2002, p. 502-513.

  81. 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.

  82. J. Han, Russ B. Altman, V. Kumar, H. Mannila, D. Pregibon Emerging Scientific Applications in Data Mining Communications of the ACM 45, 8 (August 2002), 54-58.

  83. E. Bingham, H. Mannila and J. Seppänen: Topics in 0-1 data. KDD 2002.

  84. H. Mannila: Global and local methods in data mining: basic techniques and open problems. ICALP 2002, 29th International Colloquium on Automata, Languages, and Programming, , p. 57-68, Malaga, Spain, July 2002; (c) Springer-Verlag.

  85. C.K. Leung, R. Ng, and H. Mannila: OSSM: A Segmentation Approach to Optimize Frequency Counting. Proceedings of the 18th International Conference on Data Engineering (ICDE 2002) , p. 583-593.

  86. H. Mannila, A. Patrikainen, J. Seppänen, and J. Kere: Long-range control of expression in yeast. Bioinformatics 18, 3 (2002), 482-483.

    2001

  87. 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.

  88. M. Koivisto and H. Mannila: Sibling risk and offspring risk in multilocus models. Human Heredity 51, 209-216, 2001.

  89. H. Toivonen, K. Vasko, H. Mannila, A. Korhola and H. Olander: Applying Bayesian statistics to organism-based environmental reconstruction. Ecological Applications, 11, 2, 618-630.

  90. H. Mannila and D. Rusakov: Decomposing event sequences into independent components. First SIAM Conference on Data Mining, 2001.

  91. H. Mannila and J. Seppänen: Recognizing similar situations from event sequences. First SIAM Conference on Data Mining, 2001.

  92. H. Mannila and M. Salmenkivi: Finding simple intensity descriptions from event sequence data. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2001), F. Provost and R. Srikant (eds.), p. 341-346.

  93. Johan Himberg, Kalle Korpiaho, Heikki Mannila, Johanna Tikanmäki, and Hannu T.T. Toivonen. Time series segmentation for context recognition in mobile devices In The 2001 IEEE International Conference on Data Mining (ICDM'01), 203 - 210, San Jose, California, November-December 2001. IEEE.

  94. I. Cadez, P. Smyth and H. Mannila: Probabilistic Modeling of Transaction Data with Applications to Profiling, Visualization, and Prediction. Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2001), F. Provost and R. Srikant (eds.), p. 37-46.

  95. 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.

  96. J. Mäntyjärvi, J. Himberg, P. Korpipää, and H. Mannila: Extracting the context of a mobile device user.

    2000

  97. H. Mannila and P. Smyth: Approximate query answering using frequent sets and maximum entropy. International Conference on Data Engineering, p. 309 (2000).

  98. H. Mannila: Theoretical frameworks for data mining. SIGKDD Explorations 1, 2 (January 2000), 30-32

  99. T. Laitinen, V. Ollikainen, C. Lazaro, P. Kauppi, R de Cid, J.M. Anto, X. Estivill, H. Lokki, H. Mannila, L.A. Laitinen and J. Kere: Association study of the chromosomal region containing the FCER2 gene suggests it has a regulatory role in atopic disorders. American Journal on Respiratory and Critical Care Medicine 161, 700-706, 2000.

  100. 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.

  101. 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.

  102. H. Toivonen, P. Onkamo, K. Vasko, V. Ollikainen, P. Sevon, H. Mannila, M. Herr, and J. Kere. Data mining applied to linkage disequilibrium mapping. American Journal of Human Genetics67(1): 133 - 145, July 2000.

  103. P. Kauppi, T. Laitinen, V. Ollikainen, H. Mannila, L.A. Laitinen, and J. Kere: The ILR9 region contribution in asthma is supported by genetic association in an isolated population. European Journal of Human Genetics 8, 788-792 (2000).

    1999

  104. M. Klemettinen, H. Mannila, and H. Toivonen: Exploration of interesting findings in TASA. Information and Software Technology 41, 9 (1999), 557-567.

  105. M. Klemettinen, H. Mannila, and H. Toivonen. Rule discovery in telecommunication alarm data. Journal of Network and Systems Management 7, 4 (December 1999), 395-423.

  106. I. Hovatta, T. Varilo, J. Suvisaari, J.D. Terwilliger, V. Ollikainen, R. Arajärvi, H. Juvonen, M.-L. Kokko-Sahin, L. Väisänen, H. Mannila, J. Lönnqvist and L. Peltonen: A genomewide screen for schizophrenia genes in an isolated Finnish subpopulation, suggesting multiple susceptibility loci. American Journal of Human Genetics 65, 1114-1124, 1999.

  107. M. Klemettinen, H. Mannila, and A. I. Verkamo, Association rule selection in a data mining environment. Proceedings of the Third European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'99), p. 372-377.

  108. H. Mannila and P. Moen: Similarity between Event Types in Sequences. Data Warehousing and Knowledge Discovery (DaWaK 1999), M.K. Mohania and A. M. Tjoa (eds), p. 271-280

  109. J.-F. Boulicaut, M. Klemettinen and H. Mannila: Modeling KDD Processes within the Inductive Database Framework. Data Warehousing and Knowledge Discovery (DaWaK 1999), M.K. Mohania and A. M. Tjoa (eds), p. 293-302.

  110. H. Mannila: Inductive Databases (Abstract). Inductive Logic Programming, Ninth International Workshop (ILP 1999), S. Dzeroski and P. A. Flach (eds.), p. 14.

  111. H. Mannila, D. Pavlov, and P. Smyth: Prediction with Local Patterns using Cross-Entropy. Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (KDD 1999), p. 357-361.

  112. R. Khardon, H. Mannila and D. Roth: Reasoning with Examples: Propositional Formulae and Database Dependencies. Acta Informatica 36(4): 267-286 (1999)

  113. Y. Aumann, R. Feldman, O. Liphstat and H. Mannila: Borders: An Efficient Algorithm for Association Generation in Dynamic Databases. Journal of Intelligent Information Systems 12(1), 61-73 (1999).

  114. Helge G. Gyllenberg, Mats Gyllenberg, Timo Koski, Tatu Lund, Heikki Mannila and Christopher Meek: Singling out Ill-fit Items in a Classification. Application to the Taxonomy of Enterobacteriaceae Archives of Control Sciences 9 (1999) 97-105.

  115. H. Toivonen, K. Vasko, H. Mannila, A. Korhola and H. Olander: Bayesian modeling in paleoenvironmental reconstruction. ACAI Workshop on Intelligent Techniques for Spatio-Temporal Data Analysis in Environmental Applications, p. 76-85, 1999.

  116. A. Korhola, J. Weckström, H. Seppä, H.J.B. Birks, S.M. Peglar, H. Toivonen, K. Vasko and H. Mannila: Quantitative Holocene records from sedimentary remains of aquatic organisms and pollen in northern Fennoscandia. Terra Nostra 10, 49-53, 1999.

    1998

  117. M. Eerola, H. Mannila and M. Salmenkivi: Frailty factors and time-dependent hazards in modelling ear infections. COMPSTAT'98, Conference of the International Association for Statistical Computing, R. Payne and P. Green (eds.), Springer-Verlag 1998.

  118. G. Das, H. Mannila and P. Ronkainen: Similarity of attributes by external probes. Fourth Annual Conference on Knowledge Discovery and Data Mining (KDD-98), AAAI Press, p. 16-22.

  119. G. Das, D. Lin, H. Mannila, G. Renganathan, and P. Smyth: Rule discovery from time series. Fourth Annual Conference on Knowledge Discovery and Data Mining (KDD-98), AAAI Press, p. 23-29.

  120. J.-F. Boulicaut, M. Klemettinen, and H. Mannila: Querying inductive databases: a case study on the MINE RULE operator. 2nd European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD'98), Nantes, France, September 23-26, 1998. p. 194-202.

  121. H. Mannila, H. Toivonen, A. Korhola, H. Olander: Learning, mining, or modeling? A case study in paleoecology. Discovery Science, First International Conference, Setsua Arikawa and Hiroshi Motoda (eds.), Lecture Notes in Artificial Intelligence 1532, Springer-Verlag 1998, p. 12-24. Japanese translation, to appear.

    1997

  122. D. Gunopulos, H. Mannila and S. Saluja: Discovering all most specific sentences using randomized algorithms. Database Theory -- ICDT'97, 6th International Conference, Delphi, Greece, January 1997, F. Afrati and Phokion Kolaitis, eds., 215-229.

  123. H. Mannila: Methods and problems in data mining. Database Theory -- ICDT'97, 6th International Conference, Delphi, Greece, January 1997, F. Afrati and Phokion Kolaitis, eds., p. 41--55.

  124. T. Eiter, G. Gottlob and H. Mannila: Disjunctive Datalog. ACM Transactions on Database Systems 22, 3, September 1997, 364-418.

  125. D. Gunopulos, R. Khardon, H. Mannila and H. Toivonen: Data mining, hypergraph transversals, and machine learning. Proceedings of the Sixteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS'97), p.\ 209-216, 1997.

  126. T. Eiter and H. Mannila: Distance measures for point sets and their computation. Acta Informatica, 34, 2 (1997), 109-133, 1997.

  127. G. Das, D. Gunopulos and H. Mannila: Finding similar time series. Principles of Data Mining and Knowledge Discovery (PKDD'97), Trondheim, Norwary, June 1997, Jan Komorowski and Jan Zytkow (eds.), p. 88-100.

  128. H. Mannila and P. Ronkainen: Similarity of Event Sequences. Proceedings of the Fourth International Workshop on Temporal Representation and Reasoning (TIME'97), 1997, p. 136-139.

  129. H. Mannila and H. Toivonen: Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery, 1, 3 (1997), 241-258.

  130. H. Mannila, H. Toivonen and I. Verkamo: Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery, 1,3 (1997), 259-289.

  131. H. Mannila: Inductive databases and condensed representations: concepts for data mining. International Logic Programming Symposium, 1997, p. 21-30.

  132. M. Klemettinen, H. Mannila, and H. Toivonen: A data-mining methodology and its application to semi-automatic knowledge acquisition. Proceedings of the 8th International Conference and Workshop on Database and Expert Systems Applications (DEXA'97), p.\ 670--677, Toulouse, France, September 1997.

  133. H. Ahonen, H. Mannila and E. Nikunen: Generating grammars for SGML tagged texts lacking DTD. Mathematical and Computer Modelling 26, 1 (1997), 1-13.

  134. Käki, M., Leponiemi, J., Mannila, H., Mononen, T., Räihä, K-J., and Siirtola, H.: IMIS: Intelligent Management Information System. Proceedings of the Workshop on Real-Time Intelligent User Interfaces for Decision Support and Information Visualization, Calculated Insight, Inc., pp. 20-25.

    1996

  135. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo: Fast discovery of association rules. Usama M. Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth, and Ramasamy Uthurusamy, editors, Advances in Knowledge Discovery and Data Mining, 1996. AAAI Press, p.\ 307-328.

  136. K. Hät\"onen, M. Klemettinen, H. Mannila, P. Ronkainen, and H. Toivonen: Knowledge Discovery from Telecommunication Network Alarm Databases. 12th International Conference on Data Engineering (ICDE'96), New Orleans, Louisiana, February 1996, p. 115-122.

  137. K. Hät\"onen, M. Klemettinen, H. Mannila, P. Ronkainen, and H. Toivonen: TASA: Telecommunications Alarm Sequence Analyzer, or "How to enjoy faults in your network". IEEE/IFIP 1996 Network Operations and Management Symposium (NOMS'96), Kyoto, Japan, April 1996, p. 520-529.

  138. E. Arjas, H. Mannila, M. Salmenkivi, R. Suramo and H. Toivonen: BASS: Bayesian analyzer of event sequences. COMPSTAT'96, A. Prat, ed., p. 199-204.

  139. H. Mannila: Data mining: machine learning, statistics, and databases. Eighth International Conference on Scientific and Statistical Database Management, Stockholm, June 18-20, 1996, p. 1-8.

  140. H. Mannila and H. Toivonen: On an algorithm for finding all interesting sentences. Cybernetics and Systems '96 (ECSMR '96), R. Trappl (ed.), p. 973-978.

  141. M. Jaeger, H. Mannila and E. Weydert: Data mining as selective theory extraction in probabilistic logic. SIGMOD'96 Data Mining Workshop.

  142. H. Mannila and H. Toivonen: Discovering generalized episodes using minimal occurrences. 2nd International Conference on Knowledge Discovery and Data Mining (KDD'96), Portland, Oregon, August 1996. AAAI Press, p. 146--151.

  143. H. Mannila and H. Toivonen: Multiple uses of frequent sets and condensed representations. 2nd International Conference on Knowledge Discovery and Data Mining (KDD'96), Portland, Oregon, August 1996. AAAI Press, p. 189 - 194,

  144. T. Imielinski and H. Mannila: A database perspective on knowledge discovery. Communications of the ACM 39, 11 (November 1996), 58-64.

    1995

  145. P. Kilpeläinen, H. Mannila and E. Ukkonen: MDL Learning of Unions of Simple Pattern Languages from Positive Examples. Computational Learning Theory, Second European Conference, EuroCOLT'95, Barcelona, March 1995, Paul Vitanyi (ed.), Springer-Verlag 1995, p. 252--260.

  146. M. Holsheimer, M. Kersten, H. Mannila, and H. Toivonen. A perspective on databases and data mining. First International Conference on Knowledge Discovery and Data Mining (KDD'95), 150 - 155, Montreal, Canada, August 1995. AAAI Press.

  147. H. Mannila, H. Toivonen, and A. I. Verkamo: Discovering Frequent Episodes in Sequences. First International Conference on Knowledge Discovery and Data Mining (KDD'95), 210 - 215, Montreal, Canada, August 1995. AAAI Press.

  148. P. Kilpeläinen and H. Mannila: Ordered and unordered tree inclusion. SIAM Journal on Computing 24, 2 (1995), 340-356.

  149. T. Eiter, P. Kilpeläinen and H. Mannila: Recognizing renamable generalized propositional Horn formulas is NP-complete. Discrete Applied Mathematics 59 (1995) 23-31.

  150. J. Kivinen and H. Mannila: Approximate inference of functional dependencies from relations. Theoretical Computer Science 149(1) (1995), 129-149.

    1994

  151. J. Kivinen, H. Mannila, and E. Ukkonen: Learning rules with local exceptions. In: Computational Learning Theory: EuroCOLT'93, J. Shawe-Taylor and M. Anthony (eds.), Clarendon Press, Oxford 1994, p. 35-36.

  152. H. Mannila and K.-J. Räihä: Algorithms for inferring functional dependencies from relations. Data \& Knowledge Engineering 12, 1 (February 1994), 83--99.

  153. P. Kilpeläinen and H. Mannila: Query primitives for tree-structured data. Proceedings of the 1994 Symposium on Combinatorial Pattern Matching, M. Crochemore, D. Gusfield (eds.), Springer-Verlag 1994, p. 213-225.

  154. J. Kivinen and H. Mannila: The power of sampling in knowledge discovery. Proceedings of the 1994 ACM SIGACT-SIGMOD-SIGACT Symposium on Principles of Database Theory (PODS'94), Minneapolis, MN, May 1994, p. 77-85.

  155. T. Eiter, G. Gottlob, and H. Mannila: Expressive Power and Complexity of Disjunctive Datalog. Proceedings of the 1994 ACM SIGACT-SIGMOD-SIGACT Symposium on Principles of Database Theory (PODS'94), Minneapolis, MN, May 1994, p. 267-278.

  156. H. Mannila, H. Toivonen, and I. Verkamo: Efficient algorithms for discovering association rules. Knowledge Discovery in Databases (KDD'94). U.M. Fayyad and R. Uthurusamy (eds.), AAAI Press 1994, p. 181-192.

  157. H. Ahonen, H. Mannila, and E. Nikunen, Forming grammars for structured documents: an application of grammatical inteference. Proceedings of the Second International Colloquium on Grammatical Inference and Applications, R.S. Carrasco and J. Oncina (eds.), Springer-Verlag, 1994, p. 153-167.

  158. M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen and I. Verkamo: Finding interesting rules from large sets of discovered association rules. Proceedings of the Third International Conference on Information and Knowledge Management (CIKM'94), November 1994, p. 401-407.

  159. J. Kivinen, H. Mannila, E. Ukkonen, and J. Vilo: An algorithm for learning hierarchical classifiers. ECML'94.

  160. T. Eiter, G. Gottlob, and H. Mannila: Expressive Power and Complexity of Disjunctive Datalog under the Stable Model Semantics. Management and Processing of Complex Data Structures -- Proceedings of the Third Workshop on Information Systems and Artificial Intelligence, K. van Luck and H. Marburger (eds.), Springer-Verlag 1994, p. 83--103.

    --1993

  161. R.J.R. Back and H. Mannila: Locality in modular systems. Proceedings of the Ninth International Colloquium on Automata, Languages and Programming, Aarhus, Denmark, M. Nielsen and E.M. Schmidt (eds.), Springer-Verlag 1982, p. 1-13.

  162. R.J.R. Back and H. Mannila: A refinement of Kahn's semantics to handle non-determinism and communication. Proceedings of the ACM SIGACT-SIGOPS Symposium on Principles of Distributed Computing, Ottawa, Canada, August 1982, p. 111-120.

  163. R.J.R. Back, H. Mannila and K.-J. Räihä: Derivation of efficient dag marking algorithms. Proceedings of the Tenth Annual ACM Symposium on Principles of Programming Languages, Austin, Texas, January 1983, p. 20-27.

  164. R.J.R. Back and H. Mannila: On the suitability of trace semantics for modular proofs of communicating processes. Third Conference on Foundations of Software Technology and Theoretical Computer Science, Bangalore, India, 1983, p. 40-65.

  165. H. Mannila: A topological characterization of (λ,μ)*-compactness. Annals of Pure and Applied Logic 25 (1983), 301-305.

  166. H. Mannila and K.-J. Räihä: On the relationship of minimum and optimum covers for a set of functional dependencies. Acta Informatica 20 (1983), 143-158.

  167. R.J.R. Back and H. Mannila: A semantic approach to program modularity. Information and Control 60, 1-3 (March 1984), 138-167.

  168. H. Mannila and E. Ukkonen: A simple linear-time algorithm for in situ merging. Information Processing Letters 18, 4 (May 1984), 203-208.

  169. H. Mannila: Measures of presortedness and optimal sorting algorithms. In Automata, Languages, and Programming, Eleventh Colloquium, Antwerp, Belgium, July 1984, J. Paredaens (ed.), p. 324-336.

  170. H. Mannila and D. Wood: A simple proof of the rectilinear art gallery theorem. International Journal of Computer Mathematics 17 (1985), 141-149.

  171. H. Mannila and K. Mehlhorn: A fast algorithm for renaming a set of clauses as a Horn set. Information Processing Letters 21 (1985), 269-272.

  172. H. Mannila and K.-J. Räihä: Small Armstrong relations for database design. Fourth ACM Symposium on Principles of Database Systems, Portland, Oregon, March 1985, p. 245-250.

  173. H. Mannila: Measures of presortedness and optimal sorting algorithms. IEEE Transactions on Computers C-34 (April 1985), 318-325.

  174. R.J.R. Back and H. Mannila: On the suitability of trace semantics for modular proofs of communicating processes (full version). Theoretical Computer Science 39 (1985), 47-68.

  175. H. Mannila and K.-J. Räihä: Inclusion dependencies in database design. International Conference on Data Engineering, Los Angeles, California, February 1986, p. 713-718.

  176. H. Mannila and K.-J. Räihä: Test data for relational queries. Fifth ACM SIGACT-SIGMOD Symposium on Principles of Database Systems, March 24-26, 1986, 217-223.

  177. H. Mannila and E. Ukkonen: The set union problem with backtracking. Automata, Languages and Programming, Thirteenth International Colloquium, Rennes, France, July 15-19, 1986, p. 236-243.

  178. H. Mannila and E. Ukkonen: On the complexity of unification sequences. Third International Conference on Logic Programming, July 14-18, 1986, p. 122-133.

  179. H. Mannila and E. Ukkonen: Timestamped term representation for implementing Prolog. Third IEEE Conference on Logic Programming, September 21-24, 1986, p. 159-167.

  180. H. Mannila and K.-J. Räihä: Design by example: an application of Armstrong relations. Journal of Computer and System Sciences 33, 2 (1986), 126-141.

  181. H. Mannila and E. Ukkonen: Flow analysis of Prolog programs. Proceedings, 1987 Symposium on Logic Programming, IEEE, 1987, p. 205-214.

  182. H. Mannila and K.-J. Räihä: Dependency inference. Proceedings of the 13th International Conference on Very Large Data Bases, 1987, p. 155-158.

  183. H. Mannila and E. Ukkonen: Time parameter and arbitrary deunions in the set union problem. SWAT 88: 1st Scandinavian Workshop on Algorithm Theory, R. Karlsson and A. Lingas (Eds.), Springer-Verlag 1988, p. 34-42.

  184. H. Mannila and D. Wood: A note on the largest empty rectangle problem. BIT 28 (1988), 179-183.

  185. H. Mannila and K.-J. Räihä: Automatic generation of test data for relational queries. Journal of Computer and System Sciences 38, 2 (1989), 240-258.

  186. H. Mannila and K.-J. Räihä: Practical algorithms for finding prime attributes and testing normal forms. Proceedings of the Eigth ACM SIGACT-SIGMOD-SIGART Symposium on Database and Knowledgebase Systems, March 1989, p. 128-133.

  187. P. Kilpeläinen, H. Mannila, J. Riivari, and E. Ukkonen: Prolog in Ada: an implementation and an embedding. Proceedings of AIDA-89: Fifth Annual Conference on Artificial Intelligence and Ada, November 1989, Jorge Diaz-Herrera and Jan M. Zytkow (eds.), George Mason University, p. 96-107.

  188. P. Kilpeläinen, G. Lindén, H. Mannila, and E. Nikunen: A structured document database system. EP 90: Proceendings of the International Conference on Electronic Publishing, Document Manipulation & Typography, R. Furuta (ed.), Gaithersburg, Maryland, 1990, p. 139-151.

  189. P. Kilpeläinen and H. Mannila: Generation of test cases for simple Prolog programs. Acta Cybernetica 9, 3 (1990), 235--246.

  190. H. Mannila and E. Ukkonen: Unifications, deunifications, and their complexity. BIT 30, 4 (1990), 599-619.

  191. P. Kilpeläinen and H. Mannila: The tree inclusion problem. TAPSOFT '91, S.\ Abramsky and T.S.E.\ Maibaum (eds), Brighton, England, April 1991, p.\ 202-214.

  192. M. Kantola, H. Mannila, K.-J. Räihä, and H. Siirtola: Discovering Structural Dependencies in Relational Databases. Proceedings of 1991 Workshop on Knowledge Discovery in Databases, Anaheim, Ca, July 1991, G. Piatetsky-Shapiro (ed.), p. 179-190.

  193. P. Kilpeläinen and H. Mannila: Grammatical tree matching. Combinatorial Pattern Matching, Third Workshop, A. Apostolico et al. (eds.) Tucson, Arizona, April 1992, Springer-Verlag 1992, p. 202-214.

  194. J. Kivinen, H. Mannila, and E. Ukkonen: Learning hierarchical rule sets. Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, July 27-29, 1992, p. 37-44.

  195. M. Kantola, H. Mannila, K.-J. Räihä, and H. Siirtola: Discovering Functional and Inclusion Dependencies in Relational Databases. International Journal of Intelligent Systems 7, 7 (September 1992), 591-607.

  196. J. Kivinen and H. Mannila: Approximate dependency inference from relations. Database Theory - ICDT '92, 4th International Conference, J. Biskup and R. Hull (eds.), Berlin, Germany, October 1992, p. 86-98.

  197. H. Mannila and K.-J. Räihä: On the complexity of dependency inference. Discrete Applied Mathematics 40 (1992), 237-243.

  198. V. Estivill-Castro, H. Mannila and D. Wood: Right invariant metrics and measures of presortedness. Discrete Applied Mathematics 42 (1993) 1-16.

  199. P. Kilpeläinen and H. Mannila: Retrieval from hierarchical texts using partial patterns. ACM SIGIR '93: Proceedings of the 16th Annual International Conference on Research and Development in Information Retrieval, Pittsburgh, PA, USA, June 1993, R. Korfhage, Edie Rasmussen and Peter Willett (eds.), p. 214--222.

  200. H. Ahonen, H. Mannila and E. Nikunen: Forming grammars for structured documents. Proceedings of the 1993 Workshop on Knowledge Discovery in Databases, Washington, D.C., July 1993.