Mining attributes´ sequential patterns for error identification in data set

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, attributes´ sequential pattern, is proposed. An attributes´ sequential pattern is a sequence of attributes, whose values commonly occur in ascending order over data set. After each record in data set is transformed into an attributes´ sequence according to their ordinal values, attributes´ sequential patterns can be mined by means of mining sequential patterns. But our work is different from sequential pattern mining. One use of attributes´ sequential patterns is to identify possible errors in data set for data cleaning, in which the values of attributes break the attributes´ sequential patterns which most of the data conform to. Experiments verify the high efficiency of the method presented.