Frequent Pattern Mining Based On Occupation and Correlation

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.