Applying the maximum utility measure in high utility sequential pattern mining

• We introduce a new research work, high utility sequential pattern mining with the maximum utility measure.
• We propose a projection-based PHUS approach for mining this kind of patterns.
• An effective upper-bound model is proposed to reduce search space in the mining process.
• The derived patterns are expected to be more reliable in terms of business.
• Experimental results show that the PHUS is effective in terms of efficiency and scalability.

Recently, high utility sequential pattern mining has been an emerging popular issue due to the consideration of quantities, profits and time orders of items. The utilities of subsequences in sequences in the existing approach are difficult to be calculated due to the three kinds of utility calculations. To simplify the utility calculation, this work then presents a maximum utility measure, which is derived from the principle of traditional sequential pattern mining that the count of a subsequence in the sequence is only regarded as one. Hence, the maximum measure is properly used to simplify the utility calculation for subsequences in mining. Meanwhile, an effective upper-bound model is designed to avoid information losing in mining, and also an effective projection-based pruning strategy is designed as well to cause more accurate sequence-utility upper-bounds of subsequences. The indexing strategy is also developed to quickly find the relevant sequences for prefixes in mining, and thus unnecessary search time can be reduced. Finally, the experimental results on several datasets show the proposed approach has good performance in both pruning effectiveness and execution efficiency.