Applying a pattern length constraint on the FP-Growth algorithm

With the ever-growing database sizes, we have enormous quantities of data, but unfortunately we cannot use raw data in our day-to-day reasoning/decisions. We desperately need knowledge. This knowledge is in most cases in the gathered data, but the extraction of it is a very time and resources consuming operation. In this paper we propose an improvement of the FP-Growth algorithm that focuses on applying a pattern-length constraint on the FP-Growth algorithm. This is, mining only frequent patterns with their length belonging in an interval selected by the user. The algorithm with this constraint applied can be used when only patterns with specific lengths are interesting for the user. The main advantage of running the algorithm with the length limitation instead of the classic FP-Growth algorithm is that the running time of the former is smaller, thus, the required information can be obtained in a shorter time.