Fuzzy sequential pattern mining in incomplete databases

View/Open
Document typeArticle
Defense date2008
PublisherUniversitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica
Rights accessOpen Access
Except where otherwise noted, content on this work
is licensed under a Creative Commons license
:
Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
Recent widening of data mining application areas have lead to new issues.
For instance, frequent sequence discovery techniques that were developed for
customer behaviour analysis are now applied to analyse industrial or biological
databases. Thus frequent sequence mining algorithm must be adapted
to handle particular characteristics of these data. Among these specificities
one should consider numerical attributes and incomplete data. In this paper,
we propose a method for discovering crisp or fuzzy sequential patterns from
an incomplete database. This approach uses partial information contained in
incomplete records, only temporary discarding the missing part of the record.
Experiments run on various synthetic datasets show the validity of our proposal
as well in terms of quality as in terms of the robustness to the rate of
missing values.
CitationFiot, Céline; Laurent, Anne; Teisseire, Maguelonne. Fuzzy sequential pattern mining in incomplete databases. "Mathware & Soft Computing", vol. 15, núm. 1, p. 41-59.
ISSN1134-5632
Collections
Files | Description | Size | Format | View |
---|---|---|---|---|
32-94-1-PB.pdf | 1,859Mb | View/Open |