Abstract: During the retrieval process of imbalanced stream data, the performance of data retrieval decreases due to the presence of imbalance in the data stream and the susceptibility to differential and edge data. In order to reduce the impact of the above factors, an imbalanced stream data retrieval method based on multi similarity fuzzy C-means clustering is proposed. This method calculates the multiple similarities between imbalanced flow data, and uses fuzzy C-means algorithm to cluster data with different similarities. By constructing a octree retrieval model, the data after clustering is stored, encoded and judged to complete the retrieval of unbalanced stream data. The experimental results show that the retrieval time of the proposed method is less than 20 seconds, and the recall and precision rates remain above 80% , with high NDCG( Normalized Discounted Cumulative Gain) values.

Key words: standard feature matrix, cross cluster, data encoding filter, unbalanced measure; 3D coordinates, judgment code