Interactive Knowledge Discovery and Data Mining in Biomedical Informatics : State-of-the-Art and Future Challenges

Andreas Holzinger

ISBN 10: 3662439670 / ISBN 13: 9783662439678

Editore: Springer, Springer Spektrum, 2014

Riguardo questo articolo

Druck auf Anfrage Neuware - Printed after ordering - One of the grand challenges in our digital world are the large, complex and often weakly structured data sets, and massive amounts of unstructured information. This 'big data' challenge is most evident in biomedical informatics: the trend towards precision medicine has resulted in an explosion in the amount of generated biomedical data sets. Despite the fact that human experts are very good at pattern recognition in dimensions of This state-of-the-art survey is an output of the HCI-KDD expert network and features 19 carefully selected and reviewed papers related to seven hot and promising research areas: Area 1: Data Integration, Data Pre-processing and Data Mapping; Area 2: Data Mining Algorithms; Area 3: Graph-based Data Mining; Area 4: Entropy-Based Data Mining; Area 5: Topological Data Mining; Area 6 Data Visualization and Area 7: Privacy, Data Protection, Safety and Security.

Codice articolo 9783662439678

Dati bibliografici

• Titolo: Interactive Knowledge Discovery and Data Mining in Biomedical Informatics : State-of-the-Art and Future Challenges
• Editore: Springer, Springer Spektrum
• Data di pubblicazione: 2014
• Lingua: Inglese
• ISBN 10: 3662439670
• ISBN 13: 9783662439678
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 21 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 21 millimetri di profondità
• Peso: 575 grammi

Descrizione articolo

Riassunto

One of the grand challenges in our digital world are the large, complex and often weakly structured data sets, and massive amounts of unstructured information. This “big data” challenge is most evident in biomedical informatics: the trend towards precision medicine has resulted in an explosion in the amount of generated biomedical data sets. Despite the fact that human experts are very good at pattern recognition in dimensions of <= 3; most of the data is high-dimensional, which makes manual analysis often impossible and neither the medical doctor nor the biomedical researcher can memorize all these facts. A synergistic combination of methodologies and approaches of two fields offer ideal conditions towards unraveling these problems: Human–Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human capabilities with machine learning.

This state-of-the-art survey is an output of the HCI-KDD expert network and features 19 carefully selected and reviewed papers related to seven hot and promising research areas: Area 1: Data Integration, Data Pre-processing and Data Mapping; Area 2: Data Mining Algorithms; Area 3: Graph-based Data Mining; Area 4: Entropy-Based Data Mining; Area 5: Topological Data Mining; Area 6 Data Visualization and Area 7: Privacy, Data Protection, Safety and Security.

Dalla quarta di copertina

One of the grand challenges in our digital world are the large, complex, and often weakly structured data sets and massive amounts of unstructured information. This big data challenge is most evident in biomedical informatics: The trend toward precision medicine has resulted in an explosion in the amount of biomedical data sets generated. Despite the fact that human experts are very good at pattern recognition in three dimensions or less, most of the data are high-dimensional, which makes manual analysis often impossible and neither the medical doctor nor the biomedical researcher can memorize all these facts. A synergistic combination of the methodologies and approaches of two fields offer ideal conditions for unraveling these problems: human computer interaction (HCI) and knowledge discovery/data mining (KDD), with the goal of supporting human capabilities with machine learning.

This state-of-the-art survey is an output of the HCI-KDD expert network and features 19 carefully selected and reviewed papers related to seven hot and promising research areas: (1) data integration, data pre-processing, and data mapping; (2) data mining algorithms; (3) graph-based data mining; (4) entropy-based data mining; (5) topological data mining; (6) visualization; (7) privacy, data protection, safety, and security.