Abstract
This paper presents a new efficient algorithm for clustering categorical data, Squeezer, which can produce high quality clustering results and at the same time deserve good scalability. The Squeezer algorithm reads each tuple t in sequence, either assigning t to an existing cluster (initially none), or creating t as a new cluster, which is determined by the similarities between t and clusters. Due to its characteristics, the proposed algorithm is extremely suitable for clustering data streams, where given a sequence of points, the objective is to maintain consistently good clustering of the sequence so far, using a small amount of memory and time. Outliers can also be handled efficiently and directly in Squeezer. Experimental results on real-life and synthetic datasets verify the superiority of Squeezer.
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