[1] Agrawal R, Gehrke J, Gunopulos D, et al. Automatic subspace clustering of high dimensional data for data mining applications//In Proc ACM SIGMOD Int Conf on Management of Data. Washington: ACM Press, 1998: 94-105.
[2] Agrawal R, Gehrke J, Gunopulos D, et al. Automatic subspace clustering of high dimensional data[J]. Data Mining and Knowledge Discovery, 2005, 11(1): 5-33.
[3] Cheng C H, Fu A W, Zhang Y. Entropy-based subspace clustering for mining numerical data//In Proceedings of the 5^th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. USA: ACM Press, 1999: 84-93.
[4] Goil S, Nagesh H S, Choudhary A. MAFIA: efficient and scalable subspace clustering for very large data sets[Z]. Technique Report No. CPDC-TR-9906-010, Center for Parallel and Distributed Computing, Dept of Electrical and Computer Engineering. Northwestern University: Evanston IL, 1999.
[5] Procopiuc C M, Johes M, Agarwal P K, et al. A Monte Carlo algorithm for fast projective clustering//Proc ACM SIGMOD Int Conf on Management of Data. Madison: ACM Press, 2002: 418-427.
[6] Huang Z, Ng M, Rong H. Automated variable weighting in k-means type clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(5): 657-668.
[7] Kriegel H, Krger P, Renz M, et al. A generic framework for efficient subspace clustering of high-dimensional data//Proc of 5^th IEEE Int Conf on Data Mining. New Orleans: IEEE Press, 2005: 250-257.