Frequent Temporal Pattern Mining with Extended Lists

A. Kocheturov and P. M. Pardalos
Additional contact information
A. Kocheturov: University of Florida, Center for Applied Optimization (CAO)
P. M. Pardalos: University of Florida, Center for Applied Optimization (CAO)

A chapter in Trends in Biomathematics: Modeling, Optimization and Computational Problems, 2018, pp 233-244 from Springer

Abstract: Abstract In this paper we consider Temporal Pattern Mining (TPM) for extracting predictive class-specific patterns from multivariate time series. We suggest a new approach that extends usage of the a priori property which requires a more complex pattern to appear only at places where all its subpatterns appear as well. It is based on tracking positions of a pattern inside records in a greedy manner. We demonstrate that it outperforms the previous version of the TMP on several real-life data sets independent of the way how the temporal pattern is defined.

Date: 2018
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-91092-5_16

Ordering information: This item can be ordered from
http://www.springer.com/9783319910925

DOI: 10.1007/978-3-319-91092-5_16

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().