Scalable algorithms for clustering large datasets with mixed type attributes

Author:

He Zengyou,Xu Xiaofei,Deng Shengchun

Publisher

Wiley

Subject

Artificial Intelligence,Human-Computer Interaction,Theoretical Computer Science,Software

Reference22 articles.

1. Squeezer: An efficient algorithm for clustering categorical data

2. , . C2P: Clustering based on closest pairs. In: Proc 27th Int Conf on Very Large Database, Rome, Italy. San Francisco, CA: Morgan Kaufmann; 2001. pp 331-340.

3. , , . A density-based algorithm for discovering clusters in large spatial databases. In: Proc 1996 Int Conf Knowledge Discovery and Data Mining (KDD'96), Portland, OR. Menlo Park, CA: AAAI Press; 1996. pp 226-231.

4. , . BIRCH: An efficient data clustering method for very large databases. In: Proc 1996 ACM SIGMOD Int Conf Management of Data (SIGMOD'96), Montreal, Quebec, Canada. New York: ACM Press; 1996. pp 103-114.

5. , . CURE: An efficient clustering algorithm for large databases. In: Proc 1998 ACM SIGMOD Int Conf Management of Data (SIGMOD'98), Washington, D.C. New York: ACM Press; 1998. pp 73-84.

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