HemGrupperDiskuteraMerTidsandan
Sök igenom hela webbplatsen
Denna webbplats använder kakor för att fungera optimalt, analysera användarbeteende och för att visa reklam (om du inte är inloggad). Genom att använda LibraryThing intygar du att du har läst och förstått våra Regler och integritetspolicy. All användning av denna webbplats lyder under dessa regler.

Resultat från Google Book Search

Klicka på en bild för att gå till Google Book Search.

Principles of Data Mining (Undergraduate…
Laddar...

Principles of Data Mining (Undergraduate Topics in Computer Science) (utgåvan 2007)

av Max Bramer

MedlemmarRecensionerPopularitetGenomsnittligt betygDiskussioner
59Ingen/inga439,574 (3)Ingen/inga
This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self study, this book aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. This expanded third edition includes detailed descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift.… (mer)
Medlem:m_bearach
Titel:Principles of Data Mining (Undergraduate Topics in Computer Science)
Författare:Max Bramer
Info:Springer (2007), Edition: 1, Paperback, 344 pages
Samlingar:Ditt bibliotek
Betyg:
Taggar:Ingen/inga

Verksinformation

Principles of Data Mining av Max Bramer

Ingen/inga
Laddar...

Gå med i LibraryThing för att få reda på om du skulle tycka om den här boken.

Det finns inga diskussioner på LibraryThing om den här boken.

Inga recensioner
inga recensioner | lägg till en recension
Du måste logga in för att ändra Allmänna fakta.
Mer hjälp finns på hjälpsidan för Allmänna fakta.
Vedertagen titel
Information från den engelska sidan med allmänna fakta. Redigera om du vill anpassa till ditt språk.
Originaltitel
Alternativa titlar
Första utgivningsdatum
Personer/gestalter
Viktiga platser
Viktiga händelser
Relaterade filmer
Motto
Dedikation
Inledande ord
Citat
Avslutande ord
Särskiljningsnotis
Förlagets redaktörer
På omslaget citeras
Ursprungsspråk
Kanonisk DDC/MDS
Kanonisk LCC

Hänvisningar till detta verk hos externa resurser.

Wikipedia på engelska

Ingen/inga

This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self study, this book aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. This expanded third edition includes detailed descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift.

Inga biblioteksbeskrivningar kunde hittas.

Bokbeskrivning
Haiku-sammanfattning

Pågående diskussioner

Ingen/inga

Populära omslag

Snabblänkar

Betyg

Medelbetyg: (3)
0.5
1
1.5
2
2.5
3 1
3.5
4
4.5
5

Är det här du?

Bli LibraryThing-författare.

 

Om | Kontakt | LibraryThing.com | Sekretess/Villkor | Hjälp/Vanliga frågor | Blogg | Butik | APIs | TinyCat | Efterlämnade bibliotek | Förhandsrecensenter | Allmänna fakta | 203,227,616 böcker! | Topplisten: Alltid synlig