Efficient Mining Recurring Patterns of Inter-Transaction in Time Series
Siriluck Lorpunmanee* and Suwatchai Kamonsantiroj**
*Department of Data Science and Analytics, Suan Dusit University
228-228/1-3 Sirinthon Road, Bang Bamru, Bang Phlat, Bangkok 10700, Thailand
**Department of Computer and Information Science, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok
1518 Pracharat 1 Road, Wong Sawang, Bag Sue, Bangkok 10800, Thailand
One type of the partial periodic pattern is known as recurring patterns, which exhibit cyclic repetitions only for particular time period within a series. A key property of the patterns is the event can start, stop, and restart at anytime within a series. Therefore, the extracted meaningful knowledge from the patterns is challenging because the information can vary across patterns. The mining technique in recurring patterns plays an important role for discovering knowledge pertaining to seasonal or temporal associations between events. Most existing researches focus on discovering the recurring patterns in transaction. However, these researches for mining recurring patterns cannot discover recurring events across multiple transactions (inter-transaction) which often appears in many real-world applications such as the stock exchange market, social network, etc. In this study, the proposed algorithm, namely, CP-growth can efficiently perform in discovering the recurring patterns within inter-transaction. Besides, an efficient pruning technique to reduce the computational cost of discovering recurring patterns is developed in CP-growth algorithm. Experimental results show that recurring patterns can be useful in multiple transactions and the proposed algorithm, namely, CP-growth is efficient.
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