【摘 要】
:
This paper presents a new efficient algorithm for clustering categorical data,Squeezer, which can produce high quality clustering results and at the same time d
【机 构】
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Department of Computer Science and Engineering
【基金项目】
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国家自然科学基金;IBMAS\/400 Research Fund
论文部分内容阅读
This paper presents a new efficient algorithm for clustering categorical data,Squeezer, which can produce high quality clustering results and at the same time deservegood scalability. The Squeezer algorithm reads each tuple t in sequence, either assigning tto an existing cluster (initially none), or creating t as a new cluster, which is determined bythe similarities between t and clusters. Due to its characteristics, the proposed algorithm isextremely suitable for clustering data streams, where given a sequence of points, the objective isto maintain consistently good clustering of the sequence so far, using a small amount of memoryand time. Outliers can also be handled efficiently and directly in Squeezer. Experimental resultson real-life and synthetic datasets verify the superiority of Squeezer.
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