A Projected Upper Bound for Mining High Utility Patterns from Interval-Based Event Sequences
This is an incremental improvement for researchers in data mining dealing with interval-based event sequences.
The paper tackled the efficiency problem in high utility pattern mining for interval-based event sequences by proposing a projected upper bound for pruning, which improved the HUIPMiner algorithm's execution time and memory usage.
High utility pattern mining is an interesting yet challenging problem. The intrinsic computational cost of the problem will impose further challenges if efficiency in addition to the efficacy of a solution is sought. Recently, this problem was studied on interval-based event sequences with a constraint on the length and size of the patterns. However, the proposed solution lacks adequate efficiency. To address this issue, we propose a projected upper bound on the utility of the patterns discovered from sequences of interval-based events. To show its effectiveness, the upper bound is utilized by a pruning strategy employed by the HUIPMiner algorithm. Experimental results show that the new upper bound improves HUIPMiner performance in terms of both execution time and memory usage.