  Sequential pattern mining (SPM) is an important branch of knowledge discovery
that aims to mine frequent sub-sequences (patterns) in a sequential database.
Various SPM methods have been investigated, and most of them are classical SPM
methods, since these methods only consider whether or not a given pattern
occurs within a sequence. Classical SPM can only find the common features of
sequences, but it ignores the number of occurrences of the pattern in each
sequence, i.e., the degree of interest of specific users. To solve this
problem, this paper addresses the issue of repetitive nonoverlapping sequential
pattern (RNP) mining and proposes the RNP-Miner algorithm. To reduce the number
of candidate patterns, RNP-Miner adopts an itemset pattern join strategy. To
improve the efficiency of support calculation, RNP-Miner utilizes the candidate
support calculation algorithm based on the position dictionary. To validate the
performance of RNP-Miner, 10 competitive algorithms and 20 sequence databases
were selected. The experimental results verify that RNP-Miner outperforms the
other algorithms, and using RNPs can achieve a better clustering performance
than raw data and classical frequent patterns. All the algorithms were
developed using the PyCharm environment and can be downloaded from
https://github.com/wuc567/Pattern-Mining/tree/master/RNP-Miner.
