An Algorithm to Mine Normalized Weighted Sequential Patterns Using a Prefix-projected Database

Abstract

Sequential pattern mining is an important subject in data mining with broad
applications in many different areas. However, previous sequential mining
algorithms mostly aimed to calculate the number of occurrences (the support)
without regard to the degree of importance of different data items.

In this paper, we propose to explore the search space of subsequences
with normalized weights. We are not only interested in the number
of occurrences of the sequences (supports of sequences), but also concerned
about importance of sequences (weights). When generating subsequence
candidates we use both the support and the weight of the candidates while
maintaining the downward closure property of these patterns which allows
to accelerate the process of candidate generation.