Information identification method, program product, and system using relative frequency

Abstract

In a case where supervised (learning) data is prepared and the case where test data is prepared, the data is recorded with time information attached to the data. The method includes clustering the learning data in a target class and clustering the test data in the target class. Then, the probability density for each of identified subclasses is calculated for each of time intervals having various time points and widths for the learning data, and is calculated for each of time intervals in the latest time period which have various widths, for the test data. Then, a ratio between a probability density obtained when learning is performed and a probability density obtained when testing is performed is obtained as a relative frequency in each of the time intervals for each of the subclasses. Input having a relative frequency that statistically and markedly increases is detected as an anomaly.