Patent ID: 7698740

Claim:
A method of examining sequential data by determining whether or not sequential data including a plurality of types of events belong to one or more specified categories, comprising using a computer to execute the steps of: determining one or more Eigen co-occurrence matrix sets, which are a basis for obtaining feature vectors based on a plurality of learning sequential data, converting one or more profile-learning sequential data belonging to the one or more categories into one or more profiling co-occurrence matrices, extracting one or more reference feature vectors in respect of the one or more profile-learning sequential data, based on the one or more profiling co-occurrence matrices and the one or more Eigen co-occurrence matrix sets, converting testing sequential data to be tested into a testing co-occurrence matrix, extracting a testing feature vector in respect of the testing sequential data, based on the testing co-occurrence matrix and the one or more Eigen co-occurrence matrix sets, acquiring a plurality of reference approximate co-occurrence matrices having a dimensionality reduced from that of the plurality of Eigen co-occurrence matrices, based on the one or more reference feature vectors and the plurality of Eigen co-occurrence matrices forming the one or more Eigen co-occurrence matrix sets, constructing a reference layered network model by extracting one or more events from the plurality of reference approximate co-occurrence matrices, acquiring a plurality of testing approximate co-occurrence matrices having a dimensionality reduced from that of the plurality of Eigen co-occurrence matrices, based on the testing feature vector and the plurality of Eigen co-occurrence matrices forming the one or more Eigen co-occurrence matrix sets, constructing a testing layered network model by extracting one or more events from the plurality of testing approximate co-occurrence matrices, and determining whether or not the testing sequential data to be tested belong to the one or more categories, based on the reference layered network model and the testing layered network model.