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Sinopsis
Complex data come from different application contexts. In order of handling and manage them, it is important to define suitable representation models which underly the main data features. Another challenge regards analysis systems and data exploration techniques, which support the whole Knowledge Discovery in Databases (KDD) process. Investigating and solving representation problems for complex data and defining proper algorithms and techniques to extract models, patterns and new information from such data in an effective and efficient way are the main challenges which this thesis aims to face. In particular, two main aspects have been investigated, that are the way in which complex data can be modeled (i.e., data modeling), and the way in which homogeneous groups within complex data can be identified (i.e., data clustering). The application contexts that have been objective of such studies are time series data, uncertain data, text data, and biomedical data.
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Reseña del editor
Complex data come from different application contexts. In order of handling and manage them, it is important to define suitable representation models which underly the main data features. Another challenge regards analysis systems and data exploration techniques, which support the whole Knowledge Discovery in Databases (KDD) process. Investigating and solving representation problems for complex data and defining proper algorithms and techniques to extract models, patterns and new information from such data in an effective and efficient way are the main challenges which this thesis aims to face. In particular, two main aspects have been investigated, that are the way in which complex data can be modeled (i.e., data modeling), and the way in which homogeneous groups within complex data can be identified (i.e., data clustering). The application contexts that have been objective of such studies are time series data, uncertain data, text data, and biomedical data.
Biografía del autor
Giovanni Ponti has been a researcher at ENEA since 2010. He graduated magna cum laude in Computer Engeneering at the University of Calabria in 2005, and obtained his Ph.D. in Computer Engeneering in 2010. His activities concern HPC systems, Cloud Computing and Data Mining. He has coauthored journal articles, conference papers and book chapters.
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Advances in Mining Complex Data: Modeling and Clustering
Publicado por
Lap Lambert Academic Publishing, 2013
ISBN 10: 3659305227
ISBN 13: 9783659305221
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Condición: Poor. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In poor condition, suitable as a reading copy. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,450grams, ISBN:9783659305221. Nº de ref. del artículo: 9380050
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Advances in Mining Complex Data: Modeling and Clustering
Publicado por
LAP LAMBERT Academic Publishing Jan 2013, 2013
ISBN 10: 3659305227
ISBN 13: 9783659305221
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Complex data come from different application contexts. In order of handling and manage them, it is important to define suitable representation models which underly the main data features. Another challenge regards analysis systems and data exploration techniques, which support the whole Knowledge Discovery in Databases (KDD) process. Investigating and solving representation problems for complex data and defining proper algorithms and techniques to extract models, patterns and new information from such data in an effective and efficient way are the main challenges which this thesis aims to face. In particular, two main aspects have been investigated, that are the way in which complex data can be modeled (i.e., data modeling), and the way in which homogeneous groups within complex data can be identified (i.e., data clustering). The application contexts that have been objective of such studies are time series data, uncertain data, text data, and biomedical data. 248 pp. Englisch. Nº de ref. del artículo: 9783659305221
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Advances in Mining Complex Data: Modeling and Clustering
Publicado por
LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659305227
ISBN 13: 9783659305221
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ponti GiovanniGiovanni Ponti has been a researcher at ENEA since 2010. He graduated magna cum laude in Computer Engeneering at the University of Calabria in 2005, and obtained his Ph.D. in Computer Engeneering in 2010. His activities. Nº de ref. del artículo: 5147179
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Advances in Mining Complex Data: Modeling and Clustering
Publicado por
LAP LAMBERT Academic Publishing Jan 2013, 2013
ISBN 10: 3659305227
ISBN 13: 9783659305221
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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. Neuware -Complex data come from different application contexts. In order of handling and manage them, it is important to define suitable representation models which underly the main data features. Another challenge regards analysis systems and data exploration techniques, which support the whole Knowledge Discovery in Databases (KDD) process. Investigating and solving representation problems for complex data and defining proper algorithms and techniques to extract models, patterns and new information from such data in an effective and efficient way are the main challenges which this thesis aims to face. In particular, two main aspects have been investigated, that are the way in which complex data can be modeled (i.e., data modeling), and the way in which homogeneous groups within complex data can be identified (i.e., data clustering). The application contexts that have been objective of such studies are time series data, uncertain data, text data, and biomedical data.Books on Demand GmbH, Überseering 33, 22297 Hamburg 248 pp. Englisch. Nº de ref. del artículo: 9783659305221
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Advances in Mining Complex Data: Modeling and Clustering
Publicado por
LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659305227
ISBN 13: 9783659305221
Nuevo
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Complex data come from different application contexts. In order of handling and manage them, it is important to define suitable representation models which underly the main data features. Another challenge regards analysis systems and data exploration techniques, which support the whole Knowledge Discovery in Databases (KDD) process. Investigating and solving representation problems for complex data and defining proper algorithms and techniques to extract models, patterns and new information from such data in an effective and efficient way are the main challenges which this thesis aims to face. In particular, two main aspects have been investigated, that are the way in which complex data can be modeled (i.e., data modeling), and the way in which homogeneous groups within complex data can be identified (i.e., data clustering). The application contexts that have been objective of such studies are time series data, uncertain data, text data, and biomedical data. Nº de ref. del artículo: 9783659305221
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Advances in Mining Complex Data: Modeling and Clustering
Publicado por
LAP LAMBERT Academic Publishing, 2013
ISBN 10: 3659305227
ISBN 13: 9783659305221
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Paperback. Condición: Like New. Like New. book. Nº de ref. del artículo: ERICA75836593052276
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