Title
Significance of episodes based on minimal windows Significance of episodes based on minimal windows
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Publication type
conferenceObject
Publication
Subject
Computer. Automation
Source (journal)
2009 9th IEEE International conference on data mining
Source (book)
9th IEEE International Conference on Data Mining, DEC 06-09, 2009, Miami Beach, FL
Volume/pages
(2009) , p. 513-522
ISSN
1550-4786
ISBN
978-1-4244-5242-2
ISI
000287216600053
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Discovering episodes, frequent sets of events from a sequence has been an active field in pattern mining. Traditionally, a level-wise approach is used to discover all frequent episodes. While this technique is computationally feasible it may result in a vast number of patterns, especially when low thresholds are used. In this paper we propose a new quality measure for episodes. We say that an episode is significant if the average length of its minimal windows deviates greatly when compared to the expected length according to the independence model. We can apply this measure as a post-pruning step to test whether the discovered frequent episodes are truly interesting and consequently to reduce the number of output. As a main contribution we introduce a technique that allows us to compute the distribution of lengths of minimal windows using the independence model. Such a computation task is surpisingly complex and in order to solve it we compute the distribution iteratively starting from simple episodes and progressively moving towards the more complex ones. In our experiments we discover candidate episodes that have a sufficient amount of minimal windows and test each candidate for significance. The experimental results demonstrate that our approach finds significant episodes while ignoring uninteresting ones.
E-info
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