TY - CONF
T1 - A New Insight into the Linguistic Summarization of Time Series Via a Degree of Support - Elimination of Infrequent Patterns
AU - Kacprzyk, Janusz
AU - Wilbik, Anna
N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.
PY - 2008
Y1 - 2008
N2 - We extend our previous works on using a fuzzy logic based calculus of linguistically quantified propositions for linguistic summarization of time series (cf. Kacprzyk, wilbik and zadrożny [4, 5, 6, 7, 8, 9, 10, 11, 12, 13]. That approach, using the classic degree of truth (validity) to be maximized, is here extended by adding a degree of support. On the one hand, this can reflect in natural language the essence of traditional statistical approaches, and on the other hand, can help discard linguistic summaries with a high degree of truth but a low degree of support so that they concern infrequently occurring patterns and may be uninteresting. We show an application to the absolute performance analysis of an investment (mutual) fund.keywordstime seriesfuzzy logiconline algorithmmean absolute deviationmining time series datathese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
AB - We extend our previous works on using a fuzzy logic based calculus of linguistically quantified propositions for linguistic summarization of time series (cf. Kacprzyk, wilbik and zadrożny [4, 5, 6, 7, 8, 9, 10, 11, 12, 13]. That approach, using the classic degree of truth (validity) to be maximized, is here extended by adding a degree of support. On the one hand, this can reflect in natural language the essence of traditional statistical approaches, and on the other hand, can help discard linguistic summaries with a high degree of truth but a low degree of support so that they concern infrequently occurring patterns and may be uninteresting. We show an application to the absolute performance analysis of an investment (mutual) fund.keywordstime seriesfuzzy logiconline algorithmmean absolute deviationmining time series datathese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
U2 - 10.1007/978-3-540-85027-4_47
DO - 10.1007/978-3-540-85027-4_47
M3 - Paper
SP - 393
EP - 400
ER -