This study proposes a new change detection method that leverages hubness. Hubness is a phenomenon that occurs in high-dimensional spaces, where certain special data points, known as hub data, tend to be closer to other data points. Hubness is known to degrade the accuracy of methods based on nearest neighbor search. Therefore, many studies in the past have focused on reducing hubness to improve accuracy. In contrast, this study utilizes hubness to detect changes. Specifically, if there is no change, suppressing the hubness occurring in the two datasets obtained by dividing the time series data will result in a uniform data distribution. However, if there is a change, even if we try to reduce the hubness in the two datasets obtained by dividing the time series data before and after the change, the hubness will not be reduced, and the data distribution will not become uniform. We use this finding to detect changes. Experiments with synthetic data show that the proposed method achieves accuracy comparable to or exceeding that of existing methods. Additionally, the proposed method achieves good accuracy with real-world data from hydraulic systems and gas sensors, along with excellent runtime performance.

Collaborative filtering often used in e-commerce applications, is a method to cluster similar users based on their profiles, characteristics or attitudes on specific subjects. The paper proposes a novel method to implement dynamic collaborative filtering by genetics based machine learning, in which we employ a learning classifier system extended to multiple environments. The characteristics of the dynamic collaborative filtering method are summarized as follows: (1) it is effective in distributed computer environments with PCs even for a small number of users; (2) it learns users' profiles from the individual behaviors and then generates recommendations and advice for each user; (3) the results are automatically accumulated in a local system on a PC, then they are distributed via smart IC cards while the users are interacting with the system. The method has been implemented and validated in the Group Trip Advisor prototype, a PC based distributed recommendation system for travel information.