Ke Chen

TL;DR: In this paper, a music recommendation method based on the current geographical position and physical environment of a user is proposed, where the user can obtain the optimal recommended music according to the current position and the physical environment characteristics of the user.

TL;DR: In this paper, an on-line attribute abnormal point detection method for supporting dynamic update is proposed, which comprises the following steps of: providing brand-new attribute abnormality point definition by analyzing practical application and user requirement, detecting an abnormal point on the premise of taking an attribute correlation between data points in a data set into consideration, providing more effective abnormal information relative to the traditional definition, and supporting online attribute abnormity point detection for dynamically updated stream data by combining practical stream data system application and using a sliding window and an online clustering method so as to provide real-time

TL;DR: Wang et al. as discussed by the authors proposed a blog friend recommendation method based on tree log pattern analysis, where an access log tree which takes a blog to be recommended as a root is further constructed by the technologies of grouping, sorting, loop-back removal and the like.

TL;DR: In this paper, a mining method of data stream association rules based on a sliding window is proposed, where users can determine the query conditions of association rules by setting the size and step length of the sliding window, the minimum support, confidence level and relevant error values of the association rules.