A deep data mining method for learning data based on improved genetic algorithm and DBSCAN clustering
In order to extract useful information from on-line learning big data,achieve adaptive feature extraction and clustering,and improve the granularity of learning data mining,an improved fuzzy genetic algorithm and DBSCAN clustering-based method for data mining is proposed.By transforming learning level evaluation into a text classification problem,data mining techniques are applied in the information management platform to dynamically analyze fine-grained knowledge acquisition results.The proposed genetic algorithm automatically extracts the optimal feature set from the text and associates the content of the test with the corresponding knowledge points using fuzzy rules.Finally,a density-based clustering algorithm is used to obtain global as well as individual testing performance on each knowledge point.Experimental results show that the proposed method can automatically process large amounts of data,comprehensively and accurately analyze the mastery of knowledge points from test results,which is helpful for the secondary development and in-depth mining of the data collected by the information management platform.
big datadata mininggenetic algorithmfuzzy rulestext classification
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万方数据
