Tekijä: Andreas Holzinger (ed.); Jorge Cardoso (ed.); José Cordeiro (ed.); Therese Libourel (ed.); Leszek A. Maciaszek (ed.); van S Kustantaja: Springer (2015) Saatavuus: Noin 17-20 arkipäivää
Tekijä: Andreas Holzinger (ed.); Peter Kieseberg (ed.); A Min Tjoa (ed.); Edgar Weippl (ed.) Kustantaja: Springer (2019) Saatavuus: Noin 17-20 arkipäivää
Tekijä: Andreas Holzinger (ed.); Peter Kieseberg (ed.); A Min Tjoa (ed.); Edgar Weippl (ed.) Kustantaja: Springer (2022) Saatavuus: Noin 17-20 arkipäivää
Tekijä: Andreas Holzinger (ed.); Peter Kieseberg (ed.); Federico Cabitza (ed.); Andrea Campagner (ed.); A Min Tjoa (ed.); E Weippl Kustantaja: Springer (2023) Saatavuus: Noin 17-20 arkipäivää
Tekijä: Markus Helfert (ed.); Andreas Holzinger (ed.); Martina Ziefle (ed.); Ana Fred (ed.); John O'Donoghue (ed.); Carsten Röcker Kustantaja: Springer (2015) Saatavuus: Noin 17-20 arkipäivää
Tekijä: Andreas Holzinger (ed.); Hugo Plácido Silva (ed.); Markus Helfert (ed.); Larry Constantine (ed.) Kustantaja: Springer (2022) Saatavuus: Noin 17-20 arkipäivää
Tekijä: Andreas Holzinger (ed.); Hugo Plácido da Silva (ed.); Jean Vanderdonckt (ed.); Larry Constantine (ed.) Kustantaja: Springer (2023) Saatavuus: Noin 17-20 arkipäivää
Springer Sivumäärä: 357 sivua Asu: Pehmeäkantinen kirja Painos: 2014 Julkaisuvuosi: 2014, 26.06.2014 (lisätietoa) Kieli: Englanti
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.
