| Summary: | 碩士 === 國立屏東科技大學 === 資訊管理系所 === 97 === How to discover useful knowledge from huge dataset is more and more important and difficult. Data mining is an important technique in identifying useful data. There are many methods can perform data mining for large databases in various business applications, such as decision trees, neural network, association rules, genetic algorithm and clustering algorithm. Typically, clustering schemes are classified as partitioning, hierarchical, density-based, model-based, grid-based and mixed methods.
This thesis proposes a new efficient density-based algorithm called SO-DBSCAN. According to the simulation results, the proposed SO-DBSCAN algorithm can reduce a lot of execution time comparing with two related density-based algorithms, involving DBSCAN and IDBSCAN approaches. Moreover, the presented SO-DBSCAN algorithm still has high quality clustering correctness rate and noise data filtering rate.
|
|---|
