  The aim of sequential pattern mining (SPM) is to discover potentially useful
information from a given se-quence. Although various SPM methods have been
investigated, most of these focus on mining all of the patterns. However, users
sometimes want to mine patterns with the same specific prefix pattern, called
co-occurrence pattern. Since sequential rule mining can make better use of the
results of SPM, and obtain better recommendation performance, this paper
addresses the issue of maximal co-occurrence nonoverlapping sequential rule
(MCoR) mining and proposes the MCoR-Miner algo-rithm. To improve the efficiency
of support calculation, MCoR-Miner employs depth-first search and backtracking
strategies equipped with an indexing mechanism to avoid the use of sequential
searching. To obviate useless support calculations for some sequences,
MCoR-Miner adopts a filtering strategy to prune the sequences without the
prefix pattern. To reduce the number of candidate patterns, MCoR-Miner applies
the frequent item and binomial enumeration tree strategies. To avoid searching
for the maximal rules through brute force, MCoR-Miner uses a screening
strategy. To validate the per-formance of MCoR-Miner, eleven competitive
algorithms were conducted on eight sequences. Our experimental results showed
that MCoR-Miner outperformed other competitive algorithms, and yielded better
recommendation performance than frequent co-occurrence pattern mining. All
algorithms and datasets can be downloaded from
https://github.com/wuc567/Pattern-Mining/tree/master/MCoR-Miner.
