Mining partially ordered sequential rules on unbounded data
University of New Brunswick
Data mining studies how to separate useful data from the rest. Recent work in the field of Sequential Rule Mining has led to mining new types of rules, partially ordered sequential rules. However, existing approaches do not consider the case of unbounded data. With unbounded data, conventional approaches to data mining are not sufficient because they become slow as the dataset increases in size. We propose an algorithm that makes use of previous intelligence when processing new information. This requires a slight reformulation of the problem and the solution. In the end, we show that our algorithm, on average, outperforms the existing approaches when applied to unbounded data.