Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/99592
Title: Similarity detection among data files - a machine learning approach
Authors: Dash, M.
Liu, H. 
Issue Date: 1997
Source: Dash, M.,Liu, H. (1997). Similarity detection among data files - a machine learning approach. Proceedings of the IEEE Knowledge & Data Engineering Exchange Workshop, KDEX : 172-179. ScholarBank@NUS Repository.
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.
Source Title: Proceedings of the IEEE Knowledge & Data Engineering Exchange Workshop, KDEX
URI: http://scholarbank.nus.edu.sg/handle/10635/99592
Appears in Collections:Staff Publications

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