Fuzzy C-Means Clustering for Clinical Knowledge Discovery in Databases: Optimizing FCM using Genetic Algorithm for use by Medical Experts in Diagnostic Systems and Data Integration with SchemaSQL - Couverture souple

Parthiban, Latha

 
9783639341430: Fuzzy C-Means Clustering for Clinical Knowledge Discovery in Databases: Optimizing FCM using Genetic Algorithm for use by Medical Experts in Diagnostic Systems and Data Integration with SchemaSQL
Couverture souple
ISBN 10 : 3639341430 ISBN 13 : 9783639341430
Editeur : VDM Verlag Dr. Müller, 2011

Synopsis

Data mining is the process of extracting interesting non-trivial, implicit, previously unknown and potentially useful information or patterns from data in large databases. Clustering is a data mining technique used for pattern evaluation and is the classification of similar objects into different groups, or more precisely, the partitioning of data set into subsets. The first part of the work deals with implementation of FCM clustering technique to help doctors in finding new patterns during diagnosis. FCM method is more suitable than crisp ones in clustering medical data, where imprecise conditions are the rules and FCM gives clear idea about the severity of the syndrome in a patient. Application of FCM on medical data causes class membership to become a relative one and objects can belong to many classes at same time with different degrees, which is an important feature to increase the sensitivity. A comprehensive comparative analysis also proves that FCM performs better than K-mean algorithm. The second part of the work deals with heterogeneous data integration with SchemaSQL which provides effective aggregation capabilities and also handles schematic/structural heterogeneity.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

Data mining is the process of extracting interesting non-trivial, implicit, previously unknown and potentially useful information or patterns from data in large databases. Clustering is a data mining technique used for pattern evaluation and is the classification of similar objects into different groups, or more precisely, the partitioning of data set into subsets. The first part of the work deals with implementation of FCM clustering technique to help doctors in finding new patterns during diagnosis. FCM method is more suitable than crisp ones in clustering medical data, where imprecise conditions are the rules and FCM gives clear idea about the severity of the syndrome in a patient. Application of FCM on medical data causes class membership to become a relative one and objects can belong to many classes at same time with different degrees, which is an important feature to increase the sensitivity. A comprehensive comparative analysis also proves that FCM performs better than K-mean algorithm. The second part of the work deals with heterogeneous data integration with SchemaSQL which provides effective aggregation capabilities and also handles schematic/structural heterogeneity.

Biographie de l'auteur

Latha Parthiban is currently a Professor in Computer Science and Engineering Department of SSN College of Engineering,India. She earned her B.E from Madras University, M.E from Anna University and PhD from Pondicherry University. Her current research involves applications of data mining, machine learning and image processing to real-world problems.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
VDM Verlag Dr. Müller
Date d'édition
2011
Langue
anglais
ISBN 10 
3639341430
ISBN 13 
9783639341430
Reliure
Broché
Nombre de pages
164
Coordonnées du fabricant
non disponible
Personne responsable
non disponible