Výsledky vyhledávání

Context-aware personal route recognition

Autor: Mazhelis, O., Zliobaite, I., Pechenizkiy, M., Elomaa, T., Hollmén, J., Mannila, H.

Publikováno v: Discovery Science (14th International Conference, DS 2011, Espoo, Finland, October 5-7, 2011. Proceedings), 221-235
STARTPAGE=221;ENDPAGE=235;TITLE=Discovery Science (14th International Conference, DS 2011, Espoo, Finland, October 5-7, 2011. Proceedings)
Discovery Science ISBN: 9783642244766
Discovery Science

Personal route recognition is an important element of intelligent transportation systems. The results may be used for providing personal information about location-specific events, services, emergency or disaster situations, for location-specific adv

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3820691f62818f7932884d03a46c277c
https://doi.org/10.1007/978-3-642-24477-3_19

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'Tell me more' : Finding related items from user provided feedback

Autor: Knijf, de, J., Liekens, A.M.L., Goethals, B., Elomaa, T., Hollmén, J., Mannila, H.

Publikováno v: Discovery Science (14th International Conference, DS 2011, Espoo, Finland, October 5-7, 2011. Proceedings), 76-90
STARTPAGE=76;ENDPAGE=90;TITLE=Discovery Science (14th International Conference, DS 2011, Espoo, Finland, October 5-7, 2011. Proceedings)
Discovery Science ISBN: 9783642244766
Discovery Science

The results returned by a search, datamining or database engine often contains an overload of potentially interesting information. A daunting and challenging problem for a user is to pick out the useful information. In this paper we propose an intera

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c58a7ab4e4f11fd28f1b179cd9050dc7
https://research.tue.nl/nl/publications/ce651031-71ed-4b53-a47b-6b5015373a03

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ACM TKDD Special Issue: ACM SIGKDD 2007 and ACM SIGKDD 2008

Autor: Mannila, H. Gunopulos, D.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______2127::e99f9eabf9b2d6d83282c6739234709c
https://pergamos.lib.uoa.gr/uoa/dl/object/uoadl:3028501

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Applying electromagnetic field theory concepts to clustering with constraints

Autor: Hakkoymaz, H. Chatzimilioudis, G. Gunopulos, D. Mannila, H.

This work shows how concepts from the electromagnetic field theory can be efficiently used in clustering with constraints. The proposed framework transforms vector data into a fully connected graph, or just works straight on the given graph data. Use

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______2127::9ad72074775dca70d7d9df82893887b6
https://pergamos.lib.uoa.gr/uoa/dl/object/uoadl:3064918

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A survey on condensed representations for frequent sets

Autor: Calders, T., Rigotti, C., Boulicaut, J.F., De Raedt, L., Mannila, H.

Publikováno v: Constraint-based mining and Inductive Databases : European Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March 11-13, 2004, Revised Selected Papers
Jean-François Boulicaut, Luc De Raedt, Heikki Mannila. Constraint-based mining and Inductive Databases : European Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March 11-13, 2004, Revised Selected Papers, Springer, pp.64-80, 2006, ⟨10.1007/11615576_4⟩
Constraint-Based Mining and Inductive Databases: European Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March 11-13, 2004, Revised Selected Papers, 64-80
STARTPAGE=64;ENDPAGE=80;TITLE=Constraint-Based Mining and Inductive Databases
Constraint-Based Mining and Inductive Databases ISBN: 9783540313311
Constraint-Based Mining and Inductive Databases

International audience; Solving inductive queries which have to return complete collections of patterns satisfying a given predicate has been studied extensively the last few years. The specific problem of frequent set mining from potentially huge bo

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::426eccadd73020300596f32c9f58b444
https://hal.archives-ouvertes.fr/hal-01613469

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Mining all non-derivable frequent itemsets

Autor: Calders, T., Goethals, B., Elomaa, T., Mannila, H., Toivonen, H.

Publikováno v: Principles of Data Mining and Knowledge Discovery ISBN: 9783540440376
PKDD
Principles of data mining and knowledge iscovery : proceedings 6th European conference, PKDD 2002, Helsinki, Finland, august 19-23, 2002, 74-85
STARTPAGE=74;ENDPAGE=85;TITLE=Principles of data mining and knowledge iscovery : proceedings 6th European conference, PKDD 2002, Helsinki, Finland, august 19-23, 2002
Proceedings of the 6th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD'02)

Recent studies on frequent itemset mining algorithms resulted in significant performance improvements. However, if the minimal support threshold is set too low, or the data is highly correlated, the number of frequent itemsets itself can be prohibiti

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::75672e721e9fa0b3709e32f9bd7bbdd5
http://hdl.handle.net/1942/7360

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