Hierarchical Index of High-Dimensional Point Data Based on Self-Organizing MAP

The content-based multimedia retrieval requires an effective high-dimensional point data index In this paper, a hierarchical index structure is presented, in which the self-organizing map algorithm is employed for data clustering An important proposition of class pruning its corollaries is also proposed And the nearest neighbor and k -NN searching algorithms based on these pruning conditions are also presented The experimental data indicates that the algorithm not only eliminates the possible errors in the query procedure of conventional data clustering methods, but also has very good performance in both index construction and searching