Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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Due to the huge amount of currently collected data, only computer methods are able to analyze it. Data Mining techniques could be used for this purpose, but most of currently used techniques discovering global patterns loose information about local changes. In this paper the new patterns are proposed: frequent events and groups of events in data stream. They have two advantages: information about local changes in distribution of patterns is obtained and the number of discovered patterns is smaller than in other methods. Described experiments prove that patterns give valuable knowledge, for example, in analysis of computer logs. Analysis of firewall logs reveals interest of user, its favourite web pages and used portals. By using described methods for analysis of HoneyPot logs, detailed knowledge about malicious code and time of its activity could be received. Additionally, information about infected machines IP addresses and authentication data is automatically discovered.