Discovering Fuzzy Unexpected Sequences with Concept Hierarchies

Abstract : Sequential pattern mining is the method that has received much attention in sequence data mining research and applications, however, a drawback is that it does not profit from prior knowledge of domains. In our previous work, we proposed a belief-driven method with fuzzy set theory for discovering the unexpected sequences that contradict existing knowledge of data, including occurrence constraints and semantic contradictions. In this paper, we present a new approach that discovers unexpected sequences with determining semantic contradictions by using concept hierarchies associated with the data. We evaluate the effectiveness of our approach with experiments on Web usage analysis.
Document type :
Journal articles
Complete list of metadatas

Cited literature [42 references]  Display  Hide  Download

https://hal-lirmm.ccsd.cnrs.fr/lirmm-00401364
Contributor : Haoyuan Li <>
Submitted on : Wednesday, March 20, 2019 - 4:49:33 PM
Last modification on : Thursday, March 21, 2019 - 7:52:59 PM
Long-term archiving on : Friday, June 21, 2019 - 9:57:36 PM

File

IJUFKS09-1.pdf
Publisher files allowed on an open archive

Identifiers

Collections

Citation

Haoyuan Li, Anne Laurent, Pascal Poncelet. Discovering Fuzzy Unexpected Sequences with Concept Hierarchies. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, World Scientific Publishing, 2009, 17 (supp01), pp.113-134. ⟨10.1142/S0218488509006054⟩. ⟨lirmm-00401364⟩

Share

Metrics

Record views

139

Files downloads

46