Similarity detection among data files - a machine learning approach

M. Dash, Huan Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

In any database, description files are essential to understand the data files in it. However, it is not uncommon that one is left with data files without any description file. An example is the aftermath of a system crash; other examples are related to security problems. Manual determination of the subject of a data file can be a difficult and tedious task particularly if files are look-alike. An example is a big survey database where data files that look alike are actually related to different subjects. Two data files on the same subject will probably have similar semantic structures of attributes. We detect the similarity between two attributes. Then we create clusters of attributes to compare the similarity of the subjects of two data files. And finally a machine learning technique is used to predict the subject of unseen data files.

Original languageEnglish (US)
Title of host publicationProceedings of the IEEE Knowledge & Data Engineering Exchange Workshop, KDEX
Editors Anon
Place of PublicationPiscataway, NJ, United States
PublisherIEEE
Pages172-179
Number of pages8
StatePublished - 1997
Externally publishedYes
EventProceedings of the 1997 IEEE Knowledge & Data Engineering Exchange Workshop, KDEX - Newport Beach, CA, USA
Duration: Nov 4 1997Nov 4 1997

Other

OtherProceedings of the 1997 IEEE Knowledge & Data Engineering Exchange Workshop, KDEX
CityNewport Beach, CA, USA
Period11/4/9711/4/97

ASJC Scopus subject areas

  • Engineering(all)

Fingerprint Dive into the research topics of 'Similarity detection among data files - a machine learning approach'. Together they form a unique fingerprint.

  • Cite this

    Dash, M., & Liu, H. (1997). Similarity detection among data files - a machine learning approach. In Anon (Ed.), Proceedings of the IEEE Knowledge & Data Engineering Exchange Workshop, KDEX (pp. 172-179). IEEE.