Mining ordinal patterns for data cleaning

Abstract

It is well recognized that sequential pattern mining plays an essential role in many scientific and business domains. In this paper, a new extension of sequential pattern, ordinal pattern, is proposed. An ordinal pattern is an ordinal sequence of attributes, whose values commonly occur in ascending order over data set. Ordinal pattern mining requests that values of different attributes must be comparable and ordinal. After each record in data set is transformed into an ordinal sequence of attributes according to their ordinal values, ordinal patterns can be mined by means of mining sequential patterns. But our work is different from sequential pattern mining. One use of ordinal patterns is to identify possible error records in data cleaning, in which the values of attributes break the ordinal patterns which most of the data conform to. Experiments verify the high efficiency of the method presented. ©2004 IEEE.