3 results about "Unsupervised learning" patented technology

The invention relates to a clustering algorithm technology, and discloses a gang fraud case identification method, which comprises the following steps of: acquiring multi-dimensional case portrait data; Carrying out unsupervised learning modeling on the collected case portrait data through a clustering algorithm, and dividing the cases into different case clusters; And carrying out risk scoring oneach case cluster by adopting a social network analysis algorithm, and judging an abnormal case cluster. The invention further provides an electronic device and a computer readable storage medium. According to the gang fraud case identification method, the electronic device and the computer readable storage medium provided by the invention, the gang fraud case with great similarity can be effectively identified.

The invention relates to a feedback type classification method integrating unsupervised learning and supervised learning. The method comprises the steps of classifying an original feature set throughunsupervised learning to obtain a classification result with label information; randomly and equally dividing the classification result into a training group and a control group, wherein the traininggroup serves as input of supervised learning; adopting a feature selection algorithm to enable the classification accuracy of the supervised learning to be the highest, and obtaining a corresponding feature subset; reconstructing a control group feature set, namely extracting a feature subset corresponding to the control group feature set according to the feature category of the feature subset, taking the reconstructed control group feature set as the input of the supervised learning model obtained by the training group, and calculating the classification accuracy of the supervised learning model; setting a classification accuracy threshold value as an iteration termination condition, terminating iteration if the classification accuracy is higher than a preset threshold value, obtaining aclassification result, otherwise, reconstructing an original feature set, and carrying out iteration in sequence until the preset condition is met. The classification method has good adaptability andaccuracy for the classification problem of unknown classification labels.