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Mining medical data to obtain fuzzy predicates.
Lect. Notes Comput. Sc. 8649, 103-117 (2014)
The collection of methods known as 'data mining' offers methodological and technical solutions to deal with the analysis of medical data and the construction of models. Medical data have a special status based upon their applicability to all people; their urgency (including life-or death); and a moral obligation to be used for beneficial purposes. Due to this reality, this article addresses the special features of data mining with medical data. Specifically, we will apply a recent data mining algorithm called FuzzyPred. It performs an unsupervised learning process to obtain a set of fuzzy predicates in a normal form, specifically conjunctive (CNF) and disjunctive normal form (DNF). Experimental studies in known medical datasets shows some examples of knowledge that can be obtained by using this method. Several kind of knowledge that was obtained by FuzzyPred in these databases cannot be obtained by other popular data mining techniques.
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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Herausgeber
Böhm, C.* ; Khuri, S.* ; Lhotská, L.* ; Renda, M.E.*
Schlagwörter
Fuzzy Predicates ; Knowledge Discovery ; Medical Data
ISSN (print) / ISBN
0302-9743
e-ISSN
1611-3349
ISBN
978-3-319-10264-1
Konferenztitel
Information Technology in Bio- and Medical Informatics
Konferzenzdatum
2. September 2014
Konferenzort
Munich, Germany
Zeitschrift
Lecture Notes in Computer Science
Quellenangaben
Band: 8649,
Seiten: 103-117
Verlag
Springer
Verlagsort
Berlin [u.a.]
Nichtpatentliteratur
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