Abstract

This material is posted here with permission from IEEE. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from IEEE by writing to

Citations

2159	Fast algorithms for mining association rules - Agrawal, Srikant - 1994
883	Mining frequent patterns without candidate generation - Han, Pei, et al.
262	ROCK: a robust clustering algorithm for categorical attributes - Guha, Rastogi, et al. - 1999
196	Fast algorithms for projected clustering - Aggarwal, Wolf, et al. - 1999
78	Verkamo, “Discovery of frequent episodes in event sequences - Mannila, Toivonen, et al. - 1997
56	MAFIA: Efficient and scalable subspace clustering for very large data sets - Goil, Nagesh, et al. - 1999
52	Efficiently mining long patterns from databases - Jr - 1998
43	Mining partially periodic event patterns with unknown periods - Ma, Hellerstein - 2001
42	Automated system monitoring and notification with swatch - Hansen, Atkins - 1993
24	Dimitrios Gunop-ulos, and Prabhakar Raghavan.Automatic subspace clustering of high dimensional data for data mining applications - Agrawal, Gehrke - 1998
20	The BSD Syslog Protocol - Lonvick
11	Rule Discovery in Telecommunication Alarm Data - Klementtinen, Mannila, et al. - 1999
10	Sec - a lightweight event correlation tool - Vaarandi - 2002
7	Ramakrishnan R.: ‘CACTUS - Clustering categorical data using summaries - Ganti, Gehrke - 1999
5	Survey of clustering data mining techniques. Unpublished manuscript, available from Accrue.com - Berkhin - 2002
4	Weifeng Lv, and Shilong Ma. “Intelligent Search of Correlated Alarms from Database Containing Noise Data - Zheng, Xu - 2002
3	Extending UNIX System Logging with - Bing, Erickson - 2000
2	A Systematic Approach to Discovering Correlation Rules for Event - Burns, Hellerstein, et al. - 2001