Design of Data Multi-Scale Mining Algorithm under Small Sample Machine Learning
Generally,multi-scale data mining refers to considering different scales of data information during the data mining process.In order to improve the efficiency of information processing and more accurately divide data types,this article put forward a multi-scale data mining method based on small sample machine learning algorithm.Firstly,partial actions were abstracted.Then,a website page session was generated for learning different events,thus obtaining small sample data information.Secondly,complex functions were used to build a Hibert space,and the re-producing kernel of the sample element was calculated to extract the characteristics of small sample data.Thirdly,a feature matrix was constructed by using feature vectors to adjust the balance between data,thereby obtaining the rela-tive entropy of data.Meanwhile,a multi-scale information database was built.Finally,the logical regression under machine learning was used to discretize the data feature value and mine the support degree of the indicator of the com-plex item set,thus achieving accurate multi-scale mining.Experiment results prove that the proposed method has good data classification effect,high mining accuracy,and less time consumption.
Small sample machine learning algorithmMulti-scale data miningRelative entropyCharacteristic matrixSimilarity effect
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