In this thesis, we propose a multi-level hierarchical index structure to support efficient similarity search for tagsets. The proposed method is designed based on a previous method which supports similarity search in transaction databases with a two-level bounding mechanism. Similar to the previous method, the tagsets are incrementally grouped into clusters. However, a cluster may have sub-clusters in our approach. The tagsets in a leaf-cluster are grouped into batches. Three different thresholds are used to control the degree of similarity at each level of the index structure. Furthermore, we require the tagsets in the same cluster containing at least one common tag to prevent from grouping unrelated tagsets into a cluster. The experimental results show that the proposed multi-level hierarchical index structure provides better performance on execution time of searching than both the proposed method and the naïve method significantly. Besides, with the assistant of an inverted list of clusters, the execution time of the proposed method for deletion and updating is also much better than the other two methods.

In this thesis, we propose a multi-level hierarchical index structure to support efficient similarity search for tagsets. The proposed method is designed based on a previous method which supports similarity search in transaction databases with a two-level bounding mechanism. Similar to the previous method, the tagsets are incrementally grouped into clusters. However, a cluster may have sub-clusters in our approach. The tagsets in a leaf-cluster are grouped into batches. Three different thresholds are used to control the degree of similarity at each level of the index structure. Furthermore, we require the tagsets in the same cluster containing at least one common tag to prevent from grouping unrelated tagsets into a cluster. The experimental results show that the proposed multi-level hierarchical index structure provides better performance on execution time of searching than both the proposed method and the naïve method significantly. Besides, with the assistant of an inverted list of clusters, the execution time of the proposed method for deletion and updating is also much better than the other two methods.

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