Attribute reduction: A dimension incremental strategy

Many real data sets in databases may vary dynamically. With the rapid development of data processing tools, databases increase quickly not only in rows (objects) but also in columns (attributes) nowadays. This phenomena occurs in several fields including image processing, gene sequencing and risk prediction in management. Rough set theory has been conceived as a valid mathematical tool to analyze various types of data. A key problem in rough set theory is executing attribute reduction for a data set. This paper focuses on attribute reduction for data sets with dynamically-increasing attributes. Information entropy is a common measure of uncertainty and has been widely used to construct attribute reduction algorithms. Based on three representative entropies, this paper develops a dimension incremental strategy for redcut computation. When an attribute set is added to a decision table, the developed algorithm can find a new reduct in a much shorter time. Experiments on six data sets downloaded from UCI show that, compared with the traditional non-incremental reduction algorithm, the developed algorithm is effective and efficient.