33,189 research outputs found
Mining Target-Oriented Sequential Patterns with Time-Intervals
A target-oriented sequential pattern is a sequential pattern with a concerned
itemset in the end of pattern. A time-interval sequential pattern is a
sequential pattern with time-intervals between every pair of successive
itemsets. In this paper we present an algorithm to discover target-oriented
sequential pattern with time-intervals. To this end, the original sequences are
reversed so that the last itemsets can be arranged in front of the sequences.
The contrasts between reversed sequences and the concerned itemset are then
used to exclude the irrelevant sequences. Clustering analysis is used with
typical sequential pattern mining algorithm to extract the sequential patterns
with time-intervals between successive itemsets. Finally, the discovered
time-interval sequential patterns are reversed again to the original order for
searching the target patterns.Comment: 11 pages, 9 table
Multivariate time series classification with temporal abstractions
The increase in the number of complex temporal datasets collected today has prompted the development of methods that extend classical machine learning and data mining methods to time-series data. This work focuses on methods for multivariate time-series classification. Time series classification is a challenging problem mostly because the number of temporal features that describe the data and are potentially useful for classification is enormous. We study and develop a temporal abstraction framework for generating multivariate time series features suitable for classification tasks. We propose the STF-Mine algorithm that automatically mines discriminative temporal abstraction patterns from the time series data and uses them to learn a classification model. Our experimental evaluations, carried out on both synthetic and real world medical data, demonstrate the benefit of our approach in learning accurate classifiers for time-series datasets. Copyright © 2009, Assocation for the Advancement of ArtdicaI Intelligence (www.aaai.org). All rights reserved
Using Answer Set Programming for pattern mining
Serial pattern mining consists in extracting the frequent sequential patterns
from a unique sequence of itemsets. This paper explores the ability of a
declarative language, such as Answer Set Programming (ASP), to solve this issue
efficiently. We propose several ASP implementations of the frequent sequential
pattern mining task: a non-incremental and an incremental resolution. The
results show that the incremental resolution is more efficient than the
non-incremental one, but both ASP programs are less efficient than dedicated
algorithms. Nonetheless, this approach can be seen as a first step toward a
generic framework for sequential pattern mining with constraints.Comment: Intelligence Artificielle Fondamentale (2014
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