Mathematics of data science: a computational approach to clustering and classification
"This book is on the mathematics of data science, thus the mathematical perspective will shape the presentation of the material, without forgetting the data science driver behind it. Three important themes of data science are data reduction and visualization, clustering and classification. It i...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Philadelphia
siam, Society for Industrial and Applied Mathematics
[2021]
|
| Schriftenreihe: | Data science book series
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis zbMATH |
| Zusammenfassung: | "This book is on the mathematics of data science, thus the mathematical perspective will shape the presentation of the material, without forgetting the data science driver behind it. Three important themes of data science are data reduction and visualization, clustering and classification. It is well known that behind these and numerous other techniques in data science there is mathematics that makes things work. In this book, these themes are approached, mainly, but not solely, from a linear algebra point of view. With this perspective in mind, the present book consists of a preface and twelve chapters, while at the end of the book the bibliography and an index are provided. Moreover, at the end of each chapter some notes and comments are given, which will help a reader wishing to pursue the area of each chapter further. In the sequel, the subject of each one of the twelve chapters is briefly presented."-- |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | x, 189 Seiten Illustrationen, Diagramme |
| ISBN: | 9781611976366 |
Internformat
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Datensatz im Suchindex
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| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.7 |
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| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Mathematik |
| discipline_str_mv | Informatik Mathematik |
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| id | DE-604.BV047804285 |
| illustrated | Illustrated |
| index_date | 2024-07-03T19:03:19Z |
| indexdate | 2024-07-10T09:21:48Z |
| institution | BVB |
| isbn | 9781611976366 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033187956 |
| oclc_num | 1226325540 |
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| physical | x, 189 Seiten Illustrationen, Diagramme |
| publishDate | 2021 |
| publishDateSearch | 2021 |
| publishDateSort | 2021 |
| publisher | siam, Society for Industrial and Applied Mathematics |
| record_format | marc |
| series2 | Data science book series |
| spelling | Calvetti, Daniela Verfasser (DE-588)1079495401 aut Mathematics of data science a computational approach to clustering and classification Daniela Calvetti, Case Western Reserve University, Cleveland, Ohio, Erkki Somersalo, Case Western Reserve University, Cleveland, Ohio Philadelphia siam, Society for Industrial and Applied Mathematics [2021] x, 189 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Data science book series Includes bibliographical references and index "This book is on the mathematics of data science, thus the mathematical perspective will shape the presentation of the material, without forgetting the data science driver behind it. Three important themes of data science are data reduction and visualization, clustering and classification. It is well known that behind these and numerous other techniques in data science there is mathematics that makes things work. In this book, these themes are approached, mainly, but not solely, from a linear algebra point of view. With this perspective in mind, the present book consists of a preface and twelve chapters, while at the end of the book the bibliography and an index are provided. Moreover, at the end of each chapter some notes and comments are given, which will help a reader wishing to pursue the area of each chapter further. In the sequel, the subject of each one of the twelve chapters is briefly presented."-- Big Data (DE-588)4802620-7 gnd rswk-swf Mathematik (DE-588)4037944-9 gnd rswk-swf Data Science (DE-588)1140936166 gnd rswk-swf Big data / Mathematics Information science / Mathematics Data Science (DE-588)1140936166 s Big Data (DE-588)4802620-7 s Mathematik (DE-588)4037944-9 s DE-604 Somersalo, Erkki 1960- Verfasser (DE-588)1073608077 aut 9781611976373 Erscheint auch als Online-Ausgabe, E-Book 9781611976373 B:DE-89 V:DE-601 pdf/application https://www.gbv.de/dms/tib-ub-hannover/1727399412.pdf Inhaltsverzeichnis https://zbmath.org/?q=an%3A7314144 zbMATH |
| spellingShingle | Calvetti, Daniela Somersalo, Erkki 1960- Mathematics of data science a computational approach to clustering and classification Big Data (DE-588)4802620-7 gnd Mathematik (DE-588)4037944-9 gnd Data Science (DE-588)1140936166 gnd |
| subject_GND | (DE-588)4802620-7 (DE-588)4037944-9 (DE-588)1140936166 |
| title | Mathematics of data science a computational approach to clustering and classification |
| title_auth | Mathematics of data science a computational approach to clustering and classification |
| title_exact_search | Mathematics of data science a computational approach to clustering and classification |
| title_exact_search_txtP | Mathematics of data science a computational approach to clustering and classification |
| title_full | Mathematics of data science a computational approach to clustering and classification Daniela Calvetti, Case Western Reserve University, Cleveland, Ohio, Erkki Somersalo, Case Western Reserve University, Cleveland, Ohio |
| title_fullStr | Mathematics of data science a computational approach to clustering and classification Daniela Calvetti, Case Western Reserve University, Cleveland, Ohio, Erkki Somersalo, Case Western Reserve University, Cleveland, Ohio |
| title_full_unstemmed | Mathematics of data science a computational approach to clustering and classification Daniela Calvetti, Case Western Reserve University, Cleveland, Ohio, Erkki Somersalo, Case Western Reserve University, Cleveland, Ohio |
| title_short | Mathematics of data science |
| title_sort | mathematics of data science a computational approach to clustering and classification |
| title_sub | a computational approach to clustering and classification |
| topic | Big Data (DE-588)4802620-7 gnd Mathematik (DE-588)4037944-9 gnd Data Science (DE-588)1140936166 gnd |
| topic_facet | Big Data Mathematik Data Science |
| url | https://www.gbv.de/dms/tib-ub-hannover/1727399412.pdf https://zbmath.org/?q=an%3A7314144 |
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