Frequent Pattern Mining Based On Occupation and Correlation
Kai Zhang, Yongping Zhang, Zhigang Wang · 2020
Frequent itemset mining has been extensively studied in data mining for over the last two decade because of its numerous applications. However, the classic support-based mining framework used by most previous studies is not suitable for some real-world application, such as the travel landscapes recommendation, where occupancy besides support also plays a key role in evaluating the interestingness of an itemset. In this paper, we propose a new kind of tasks based on occupancy, namely high correlated occupancy mining, by introducing correlated occupancy into the support-based mining framework. We present the confidence constraint to filter redundant information and show the mining goal of top-k quality pattern combined with occupancy and correlation pattern mining algorithm to ensure the results credible.