  Based on the analysis of the proportion of utility in the supporting
transactions used in the field of data mining, high utility-occupancy pattern
mining (HUOPM) has recently attracted widespread attention. Unlike high-utility
pattern mining (HUPM), which involves the enumeration of high-utility (e.g.,
profitable) patterns, HUOPM aims to find patterns representing a collection of
existing transactions. In practical applications, however, not all patterns are
used or valuable. For example, a pattern might contain too many items, that is,
the pattern might be too specific and therefore lack value for users in real
life. To achieve qualified patterns with a flexible length, we constrain the
minimum and maximum lengths during the mining process and introduce a novel
algorithm for the mining of flexible high utility-occupancy patterns. Our
algorithm is referred to as HUOPM+. To ensure the flexibility of the patterns
and tighten the upper bound of the utility-occupancy, a strategy called the
length upper-bound (LUB) is presented to prune the search space. In addition, a
utility-occupancy nested list (UO-nlist) and a frequency-utility-occupancy
table (FUO-table) are employed to avoid multiple scans of the database.
Evaluation results of the subsequent experiments confirm that the proposed
algorithm can effectively control the length of the derived patterns, for both
real-world and synthetic datasets. Moreover, it can decrease the execution time
and memory consumption.
