| Summary: | 碩士 === 國立屏東科技大學 === 資訊管理系所 === 100 === Of the many data clustering algorithms proposed in recent years, the most effective are the density-based clustering algorithms, DBSCAN and IDBSCAN. Although density-based clustering method is effective for identifying graphs, filtering out noise, and obtaining good clustering results, it is extremely time consuming. The IDBSCAN is faster than DBSCAN but is still unsatisfactory.
This thesis therefore developed QIDBSCAN (Quick IDBSCAN), a new data clustering algorithm based on IDBSCAN that uses MBOs (Marked Boundary Objects) to expand computing directly without an actual data points selection. The experimental results in this study confirm that QIDBSCAN is substantially faster than IDBSCAN, DBSCAN, and other density-based algorithms.
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