Duomenų gavybos technologija

Gruodis, Rimvydas; Salys, Donatas

Use this URL to cite this publication: https://hdl.handle.net/20.500.14911/141506

Duomenų gavybos technologija

Details

Publication Type (CRIS)

Straipsnis nerecenzuojamoje Lietuvos konferencijos medžiagoje / Article in non-peer-reviewed Lithuanian conference proceedings (P2c)

Publication Type (eLABa)

Straipsnis nerecenzuotame konferencijos darbų leidinyje / Article in an un-reviewed conference proceedings (P2)

Author(s)

Author	Affiliation
Gruodis, Rimvydas	Fundamentinių mokslų fakultetas / Faculty of Fundamental Sciences (10)
Salys, Donatas	Fundamentinių mokslų fakultetas / Faculty of Fundamental Sciences (10)

Title

Duomenų gavybos technologija

[lt]

Rimvydas Gruodis, Donatas Salys

Title in other language

Data mining

[en]

Is part of

Matematika (2006 m.balandžio 20 d.). Informatika (2006 m. balandžio 12-13 d.d.): 9-osios Lietuvos jaunųjų mokslininkų konferencijos Mokslas - Lietuvos ateitis medžiaga

Published In

Year	Start Page	End Page
2006	412	417

Publisher

Vilnius : Technika, 2006

Extent

p. 412-417

URI

URI
https://hdl.handle.net/20.500.14911/141506

Cited references

Science / Art Area

Technologijos mokslai / Technological sciences (T)

Field of Science / Art

Informatikos inžinerija / Informatics engineering (T007)

Abstract (en)

Databases today can range in size into the terabytes - more than 1,000,000,000,000 bytes of data. Wthin these asses of data lies hidden information of strategic importance. But when there are somany trees, how do you draw meaningful conclusions about the forest? The newest answer is data mining, which is being used both to increase revenues and to reduce costs. The popential returns are enormous. Innovative organizations wordwide are already using data mining to locate and appeal higher- value customers, to reconfigure their product offerings to increase sales, and to minimiize losses due to error to fraud. Data minning is a process that uses a variety of data analysis tools to discover patterns and relationships in data that may be used to make valid predictions.