Earticle

Frequent graph mining is one of famous data mining fields that receive the most attention, and its importance has been raised continually as recent databases in the real world become more complicated. Weighted frequent graph mining is an approach for applying importance of objects in the real world to the graph mining, and numerous studies related to this have been conducted so far. However, all of the results obtained from this approach do not become actually useful information, and a significant portion of them may be meaningless ones even though they are weighted frequent sub-graph patterns. To overcome this problem, in this paper, we propose a novel method which can consider whether any sub-graph pattern has close correlation among elements in the pattern, called MSCG (Mining Strongly Correlated sub-Graph). In experimental results, we demonstrate that our MSCG outperforms a state-of-the-art method with respect to runtime and memory usage.

This paper proposes a new weighted mining frequent pattern based on customer’s RFM(Recency, Frequency, Monetary) score for personalized u-commerce recommendation system under ubiquitous computing. An existing recommendation system using traditional mining has the problem, such as delay of processing speed from a cause of frequent scanning a large data, considering equal weight value of every item, and accuracy as well. In this paper, to solve these problems, it is necessary for us to extract the most frequently purchased data from whole data, to consider the weight/importance of attribute of item in order to forecast frequently changing trends by emphasizing the important items with high purchasability and to improve the accuracy of personalized u-commerce recommendation. To verify improved performance, we make experiments with dataset collected in a cosmetic internet shopping mall.