Publication
Title
Significance of episodes based on minimal windows
Author
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.
Language
English
Source (journal)
Proceedings. - Los Alamitos, Calif, 2001, currens
Source (book)
9th IEEE International Conference on Data Mining, DEC 06-09, 2009, Miami Beach, FL
Publication
Los Alamitos, Calif : IEEE Computer Society, 2009
Volume/pages
(2009), p. 513-522
ISI
000287216600053
Number
978-1-4244-5242-2
Full text (Publisher's DOI)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identification
Creation 22.01.2014
Last edited 16.06.2017
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