Abstract
Association rules can detect the association pattern between POIs (point of interest) and serve the application of indoor location. In this paper, a new index, tuple-relation, is defined, which reflects the association strength between POI sets in indoor environment. This index considers the potential association information such as spatial and semantic information between indoor POI sets. On this basis, a new R-FP-growth (tuple-relation frequent pattern growth) algorithm for mining association rules in indoor environment is proposed, which makes comprehensive use of the co-occurrence probability, conditional probability, and multiple potential association information among POI sets, to form a new support-confidence-relation constraint framework and to improve the quality and application value of mining results. Experiments are performed, using real Wi-Fi positioning trajectory data from a shopping mall. Experimental results show that the tuple-relation calculation method based on cosine similarity has the best effect, with an accuracy of 87%, and 19% higher than that of the traditional FP-growth algorithm.
Highlights
As one of the core tasks in the field of data mining, association rules [1] can get the association patterns between different data itemsets in the data set, so as to find out the internal commonness of user behavior habits [2]
The application of association rule algorithm in indoor environment can capture the interdependence of indoor POI, which plays an important role in indoor personalized information recommendation, indoor marketing strategy formulation and indoor user behavior pattern mining
WORK This paper proposes a new method for mining POI association rules based on tuple-relation in indoor environment
Summary
As one of the core tasks in the field of data mining, association rules [1] can get the association patterns between different data itemsets in the data set, so as to find out the internal commonness of user behavior habits [2]. The core of association rules is to detect the interdependency and association between one or more than one item and other items. In the indoor environment, occupying more than 70% of human activities [7]–[9], users move and consume between indoor POIs(point of interest), reflecting the behavior and purchase habits of indoor users. The application of association rule algorithm in indoor environment can capture the interdependence of indoor POI, which plays an important role in indoor personalized information recommendation, indoor marketing strategy formulation and indoor user behavior pattern mining.
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