(Enter summary)
Abstract: We introduce a methodology for knowledge discovery
in databases (KDD) where one first discovers large
collections of patterns at once, and then performs interactively
retrieves subsets of the collection of patterns.
The proposed methodology suits such KDD
formalisms as association and episode rules, where
large collections of potentially interesting rules can be
found efficiently.
We present methods that support interactive exploration
of large collections of rules. With these methods
the user... (Update)
Similar documents (at the sentence level): More
32.3%: A Knowledge Discovery Methodology for Telecommunication Network .. - Klemettinen (1999)
(Correct)
19.9%: Interactive Exploration of Discovered Knowledge: A.. - Klemettinen.. (1996)
(Correct)
8.4%: Rule Discovery in Telecommunication Alarm Data - Klemettinen, Mannila, Toivonen (1999)
(Correct)
Active bibliography (related documents): More All
0.5: Potential Applications of Granular Computing in Knowledge.. - Yao, Zhong (1999)
(Correct)
0.3: Rule Discovery in Alarm Databases - Hätönen, Klemettinen, Mannila.. (1996)
(Correct)
0.3: A Model For Alarm Correlation in Telecommunications Networks - Meira (1997)
(Correct)
Similar documents based on text: More All
0.5: TreeDT: Gene Mapping by Tree Disequilibrium Test - Sevon, Toivonen, Ollikainen (2001)
(Correct)
0.5: Mining Sequential Alarm Patterns in a Telecommunication Database - Wu, Peng, Chen (2001)
(Correct)
0.5: Network Fault Detection: A Simplified Approach To Alarm.. - Gardner, Harle
(Correct)
BibTeX entry: (Update)
@inproceedings{ klemettinen97data,
author = "Mika Klemettinen and Heikki Mannila and Hannu Toivonen",
title = "A Data Mining Methodology and Its Application to Semi-automatic Knowledge Acquisition",
booktitle = "{DEXA} Workshop",
pages = "670-677",
year = "1997",
url = "citeseer.ist.psu.edu/2695.html" }
Citations (may not include all citations):
921
Mining association rules between sets of items in large data..
- Agrawal, Imielinski et al. - 1993
400
Fast discovery of association rules (context) - Agrawal, Mannila et al. - 1996
262
From data mining to knowledge discovery: An overview (context) - Fayyad, Piatetsky-Shapiro et al. - 1996
189
Discovering frequent episodes in sequences (context) - Mannila, Toivonen et al. - 1995
106
The KDD process for extracting useful knowledge from volumes..
- Fayyad, Piatetsky-Shapiro et al. - 1996
85
Discovering generalized episodes using minimal occurrences
- Mannila, Toivonen - 1996
36
Knowledge discovery from telecommunication network alarm dat..
- Hatonen, Klemettinen et al. - 1996
36
Alarm correlation (context) - Jakobson, Weissman - 1993
36
Pruning and grouping of discovered association rules
- Toivonen, Klemettinen et al. - 1995
26
an algorithm for finding all interesting sentences (context) - Mannila, Toivonen - 1996
26
Finding interesting rules from large sets of discovered asso.. (context) - Klemettinen, Mannila et al. - 1994
17
Selecting and reporting what is interesting (context) - Matheus, Piatetsky-Shapiro et al. - 1996
14
Efficient discovery of interesting statements in databases (context) - Kloesgen - 1995
9
The process of knowledge discovery in databases: A first ske.. (context) - Brachman, Anand - 1994
3
Noaa -- an expert system managing the telephone network (context) - Goodman, Ambrose et al. - 1995
2
From contingency (context) - Zembowicz, Zytkow
Documents on the same site (http://rolfpc26.pc.helsinki.fi/~htt/pubs/index.html): More
Efficient Algorithms for Discovering Association Rules - Mannila, Toivonen, Verkamo (1994)
(Correct)
Finding Frequent Substructures in Chemical Compounds - Dehaspe, Toivonen, King (1998)
(Correct)
Data mining, Hypergraph Transversals, and Machine.. - Gunopulos, Khardon, al. (1997)
(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