Heikki Mannila, Hannu Toivonen  Home/Search
Context Related
| cs.helsinki.fi/researc...C19978.ps.gz utexas.edu/course/ee380l...assoc7.ps.gz cs.helsinki.fi/researc...C19978.ps.gz Cached: PS.gz PS PDF This document uses CoBlitz to cache paper downloads. If your firewall is blocking outgoing connections to port 3125, you can use these links to download local copies.
Image Update HelpPS.gz PS PDF From: cs.helsinki.fi/research/fdk/da... (more) From: cs.helsinki.fi/...ngpublications Homepages: H.Mannila H.Toivonen |
Rate this article:      (best)Comment on this article |
Abstract: One of the basic problems in knowledge discovery in databases (KDD) is the following: given a data set r, a class L of sentences for defining subgroups of r, and a selection predicate, find all sentences of L deemed interesting by the selection predicate. We analyze the simple levelwise algorithm for finding all such descriptions. We give bounds for the number of database accesses that the algorithm makes. For this, we introduce the concept of the border of a theory, a notion that turns out to... (Update)
Cited by: More
Catch the Moment: Maintaining Closed Frequent Itemsets - Over Data Stream (Correct)
Distribution-Based Synthetic Database Generation.. - Ganesh Ramesh University (Correct)
Temporal Knowledge Discovery with Infrequent Episodes - Dan Li Liying (2004) (Correct)
Similar documents (at the sentence level): More
29.1%: Data Mining and Knowledge Discovery KL503-02-Mannila-1.. - Data Mining And (Correct)
21.5%: On an algorithm for finding all interesting sentences.. - Mannila, Toivonen (1996) (Correct)
5.6%: Discovering All Most Specific Sentences - Gunopulos, Khardon, Mannila.. (2003) (Correct)
Active bibliography (related documents): More All
0.5: Methods and Problems in Data Mining - Mannila (1997) (Correct)
0.5: Pruning Closed Itemset Lattices for Association Rules - Pasquier, Bastide, Taouil.. (1998) (Correct)
0.4: Discovering all Most Specific Sentences by Randomized.. - Gunopulos, al. (1997) (Correct)
Similar documents based on text: More All
0.7: Discovering Generalized Episodes Using Minimal Occurrences - Mannila, Toivonen (1996) (Correct)
0.7: Discovery of Frequent Episodes in Event Sequences - Mannila, Toivonen, Verkamo (1997) (Correct)
0.6: Discovery of Frequent Patterns in Large Data Collections - Toivonen (1996) (Correct)
Related documents from co-citation: More All
46: Mining association rules between sets of items in large databases - Agrawal, Imielinski et al. - 1993
45: Fast discovery of association rules (context) - Agrawal, Mannila et al. - 1996
34: Fast Algorithms for Mining Association Rules - Agrawal, Srikant - 1994
BibTeX entry: (Update)
Mannila, H., and Toivonen, H. 1997. Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery 1(3):241 -- 258. http://citeseer.ist.psu.edu/mannila97levelwise.html More
@article{ mannila97levelwise,
author = "Heikki Mannila and Hannu Toivonen",
title = "Levelwise Search and Borders of Theories in Knowledge Discovery",
journal = "Data Mining and Knowledge Discovery",
volume = "1",
number = "3",
pages = "241-258",
year = "1997",
url = "citeseer.ist.psu.edu/mannila97levelwise.html" }
Citations (may not include all citations):921 Mining association rules between sets of items in large data.. - Agrawal, Imielinski et al. - 1993
474 Advances in Knowledge Discovery and Data Mining (context) - Fayyad, Piatetsky-Shapiro et al. - 1996
400 Fast discovery of association rules (context) - Agrawal, Mannila et al. - 1996
274 Generalization as search (context) - Mitchell - 1982
268 Mining generalized association rules - Srikant, Agrawal - 1995
213 Discovery of multiple-level association rules from large dat.. - Han, Fu - 1995
209 Mining quantitative association rules in large relational ta.. - Srikant, Agrawal - 1996
189 Sampling large databases for association rules - Toivonen - 1996
189 Discovering frequent episodes in sequences (context) - Mannila, Toivonen et al. - 1995
160 Knowledge Discovery in Databases (context) - Piatetsky-Shapiro, Frawley - 1991
137 Finding interesting rules from large sets of discovered asso.. - Klemettinen, Mannila et al. - 1994
103 Elements of Machine Learning (context) - Langley - 1996
85 Discovering generalized episodes using minimal occurrences - Mannila, Toivonen - 1996
69 the complexity of dualization of monotone disjunctive normal.. (context) - Fredman, Khachiyan - 1996
68 Controlling the complexity of learning in logic through synt.. - Kietz, Wrobel - 1992
67 Identifying the minimal transversals of a hypergraph and rel.. - Eiter, Gottlob - 1995
60 A theory of clausal discovery (context) - De Raedt, Bruynooghe - 1993
52 and presentation of strong rules (context) - Piatetsky-Shapiro, analysis - 1991
51 First-order jk-clausal theories are PAClearnable (context) - De Raedt, eroski - 1994
47 Set-oriented mining of association rules (context) - Houtsma, Swami - 1993
46 A perspective on databases and data mining - Holsheimer, Kersten et al. - 1995
45 Design by example: An application of Armstrong relations (context) - Mannila, ih - 1986
41 Mining optimized association rules for numeric attributes (context) - Fukuda - 1996
34 and machine learning (context) - Gunopulos, Khardon et al. - 1996
26 North-Holland Publishing Company (context) - Berge, of et al. - 1973
23 Discovering functional and inclusion dependencies in relatio.. (context) - Kantola, Mannila et al. - 1992
23 Algorithms for inferring functional dependencies (context) - Mannila, ih - 1994
18 On generating the irredundant conjunctive and disjunctive no.. (context) - Gurvich, Khachiyan - 1995
13 Compression-based evaluation of partial determinations - Pfahringer, Kramer - 1995
12 Discovery and maintenance of functional dependencies by inde.. - Bell - 1995
4 Discovering foreign key relations in relational databases (context) - Knobbe, Adriaans - 1996
3 On bounded-degree hypergraph transversal (context) - Mishra, Pitt - 1995
3 the complexity of dependency inference (context) - Mannila, ih - 1992
2 An eOEcient algorithm for mining association rules in large .. (context) - Savasere, Omiecinski et al. - 1995
1 An eoeective hash-based algorithm for mining association rul.. (context) - Park, Chen et al. - 1995
1 AddisonWesley Publishing Company (context) - Mannila, ih et al. - 1992
1 EOEcient discovery of interesting statements in databases (context) - Kloesgen - 1995
The graph only includes citing articles where the year of publication is known.
Documents on the same site (http://www.cs.helsinki.fi/research/fdk/datamining/pubs/): More
Pruning and Grouping Discovered Association Rules - Toivonen, Klemettinen.. (1995) (Correct)
Automatic generation of SGML content models - Ahonen (1995) (Correct)
TASA: Telecommunication Alarm Sequence Analyzer.. - Hätönen.. (1996) (Correct)
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC