We developed a method for extracting feature patterns from multivariate data using a network of coupled phase oscillators subject to an analogue of the Kuramoto model for collective synchronization. Our method may be called data synchronization. We applied data synchronization to the care-needs-certification data, provided by Otsu City as a historical old city near Kyoto City, in the Japanese public long-term care insurance program to find the trend of the major patterns of the aging process for elderly people needing nursing care.

We have recently developed a method for feature extraction from multivariate data using an analogue of Kuramoto's dynamics for modeling collective synchronization in a network of coupled phase oscillators. In our method, which we call data synchronization, phase oscillators carrying multivariate data in their natural and updated rhythms achieve partial synchronizations. Their common rhythms are interpreted as the template vectors representing the general features of the data set. In this study, we discuss the link of data synchronization to the self-organizing map algorithm as a popular method for data mining and show through numerical experiments how our method can overcome the disadvantages of the self-organizing map algorithm in that unintentional selections of inappropriate reference vectors lead to false feature patterns.