# Data-Mining. Perspective de abordare (Data-Mining. Aproaching Perspectives)

The workshop The actual problems of Statistics, The Higher Education in Statistics and the Bologna process Iasi (2005)

10 Pages Posted: 24 Jan 2014

See all articles by Daniel Homocianu

## Daniel Homocianu

Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, Department of Accounting, Business Information Systems and Statistics

## Catalin Grama

Siemens VDO Automotive

Date Written: 2005

### Abstract

Romanian Abstract: La ora actuala, mari volume de date se acumuleaza si cantitatea lor se spune ca se dubleaza la fiecare noua luni. De aceea, cautarea cunoasterii in date in cantitati mari devine una dintre cele mai dorite atribute ale Data Mining -- "mineritului datelor". Datele sunt de fapt in cantitati mari in 2 feluri: ca marime -- datele imagine -- sau ca dimensionalitate -- date expresive.

Exista de asemenea o prapastie uriasa intre datele stocate si cunoasterea care poate fi interpretata din date. Aici intervine Data Mining. In analiza exploratorie exista o anumita cunoastere initiala despre date, dar "mineritul datelor" ar putea sa ajute la cunoasterea in profunzime a datelor. Este la fel de adevarat ca anumite feluri de a analiza datele exista de foarte multa vreme, dar ele creaza strangulari cand vine vorba de volume mari de date.

Pe de alta parte, informatica, tehnicile de inginerie si metodologiile in rapida dezvoltare genereaza cerinte noi. Tehnicile Data Mining sunt aplicate acum in multe domenii bogate in date. Astfel, cateva perspective de abordare privesc: Statistica, Bazele de date relationale, Inteligenmta artificiala si Vizualizarea datelor.)

English Abstract: Nowadays, large quantities of data are being accumulated and the collection is said to be almost doubled every nine months. Thus seeking knowledge from massive data becomes one of the most desired attributes of Data-Mining. Data could be massive in two ways: as size -- image data -- or as dimensionality -- expression data.

There is also a huge gap from the stored data to the knowledge that could be interpreted from the data. That is where Data Mining comes into picture. In Exploratory Data Analysis, some initial knowledge about the data is present, but Data Mining could help in a more in-depth knowledge about the data. It is also true that a kind of data analysis has been around for some time now, but it creates a bottleneck for large data analysis.

Otherwise fast developing computer science and engineering techniques and methodology generates new demands. Data-Mining techniques are now being applied to all kinds of domains, which are rich in data. Thus, a few approach perspectives are: Statistics, Relational Databases, Artificial Intelligence, Visualization.

Keywords: data-mining, perspectives, Statistics, Relational Databases, Artificial Intelligence, Visualization

JEL Classification: O33

Suggested Citation

Homocianu, Daniel and Grama, Catalin, Data-Mining. Perspective de abordare (Data-Mining. Aproaching Perspectives) (2005). The workshop The actual problems of Statistics, The Higher Education in Statistics and the Bologna process Iasi (2005). Available at SSRN: https://ssrn.com/abstract=2383092

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