Ten lectures on wavelets
Fast discovery of association rules
Advances in knowledge discovery and data mining
TOPIC ISLANDS—a wavelet-based text visualization system
Proceedings of the conference on Visualization '98
Algorithm 457: finding all cliques of an undirected graph
Communications of the ACM
Finding simple intensity descriptions from event sequence data
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Mining Partially Periodic Event Patterns with Unknown Periods
Proceedings of the 17th International Conference on Data Engineering
Mining Surprising Patterns Using Temporal Description Length
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
WaveCluster: a wavelet-based clustering approach for spatial data in very large databases
The VLDB Journal — The International Journal on Very Large Data Bases
A survey on wavelet applications in data mining
ACM SIGKDD Explorations Newsletter
Pattern discovery in sequences under a Markov assumption
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Data-driven validation, completion and construction of event relationship networks
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
An integrated framework on mining logs files for computing system management
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Discovering Frequent Episodes and Learning Hidden Markov Models: A Formal Connection
IEEE Transactions on Knowledge and Data Engineering
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
Event summarization for system management
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A minimum description length objective function for groupwise non-rigid image registration
Image and Vision Computing
Constructing comprehensive summaries of large event sequences
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Trajectory Outlier Detection: A Partition-and-Detect Framework
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
One Graph Is Worth a Thousand Logs: Uncovering Hidden Structures in Massive System Event Logs
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Substructure discovery using minimum description length and background knowledge
Journal of Artificial Intelligence Research
Constructing comprehensive summaries of large event sequences
ACM Transactions on Knowledge Discovery from Data (TKDD)
Predictive algorithms in the management of computer systems
IBM Systems Journal
An algorithmic approach to event summarization
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
An MDL approach to efficiently discover communities in bipartite network
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Data summarization model for user action log files
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
MOETA: a novel text-mining model for collecting and analysing competitive intelligence
International Journal of Advanced Media and Communication
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Event mining is a useful way to understand computer system behaviors. The focus of recent works on event mining has been shifted to event summarization from discovering frequent patterns. Event summarization seeks to provide a comprehensible explanation of the event sequence on certain aspects. Previous methods have several limitations such as ignoring temporal information, generating the same set of boundaries for all event patterns, and providing a summary which is difficult for human to understand. In this paper, we propose a novel framework called natural event summarization that summarizes an event sequence using inter-arrival histograms to capture the temporal relationship among events. Our framework uses the minimum description length principle to guide the process in order to balance between accuracy and brevity. Also, we use multi-resolution analysis for pruning the problem space. We demonstrate how the principles can be applied to generate summaries with periodic patterns and correlation patterns in the framework. Experimental results on synthetic and real data show our method is capable of producing usable event summary, robust to noises, and scalable.