Discovering Association Rules on Experiences from Large-Scale Blog Entries

Autor:	Takeshi Kurashima, Ko Fujimura, Hidenori Okuda
Rok vydání:	2009
Předmět:	Set (abstract data type) World Wide Web Information retrieval Association rule learning Computer science Scale (social sciences) Period (music)
Zdroj:	Lecture Notes in Computer Science ISBN: 9783642009570 ECIR
DOI:	10.1007/978-3-642-00958-7_49
Popis:	This paper proposes a method for discovering association rules on peoples' experiences extracted from a large-scale set of blog entries. In our definition, a person's experience can be expressed by five attributes: time, location, activity, opinion and emotion. The system implementing our proposed method actually generates and ranks association rules between attributes by applying several interestingness measures proposed in the area of data mining to the experiences extracted from 48 million blog entries. An experiment shows that the system successfully mines peoples' activities and emotions which are specific to location and time period.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::37b44f9ec52d01ce96a89280a54aa1c7 https://doi.org/10.1007/978-3-642-00958-7_49 Zobrazit plný text záznamu