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» Phenomenal Data Mining: From Data to Phenomena
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MDAI
2007
Springer
16 years 20 days ago
Mining Frequent Diamond Episodes from Event Sequences
In this paper, we introduce a diamond episode of the form s1 → E → s2, where s1 and s2 are events and E is a set of events. The diamond episode s1 → E → s2 means that every...
Takashi Katoh, Kouichi Hirata, Masateru Harao
DIS
2006
Springer
15 years 10 months ago
Mining Sectorial Episodes from Event Sequences
In this paper, we introduce a sectorial episode of the form C r, where C is a set of events and r is an event. The sectorial episode C r means that every event of C is followed b...
Takashi Katoh, Kouichi Hirata, Masateru Harao
ACL
2008
15 years 8 months ago
Mining Parenthetical Translations from the Web by Word Alignment
Documents in languages such as Chinese, Japanese and Korean sometimes annotate terms with their translations in English inside a pair of parentheses. We present a method to extrac...
Dekang Lin, Shaojun Zhao, Benjamin Van Durme, Mari...
BMCBI
2006
126views more  BMCBI 2006»
15 years 6 months ago
Effect of data normalization on fuzzy clustering of DNA microarray data
Background: Microarray technology has made it possible to simultaneously measure the expression levels of large numbers of genes in a short time. Gene expression data is informati...
Seo Young Kim, Jae Won Lee, Jong Sung Bae
DGO
2007
192views Education» more  DGO 2007»
15 years 8 months ago
D-HOTM: distributed higher order text mining
We present D-HOTM, a framework for Distributed Higher Order Text Mining based on named entities extracted from textual data that are stored in distributed relational databases. Unl...
William M. Pottenger