PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
What's hot and what's not: tracking most frequent items dynamically
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Aurora: a data stream management system
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
A symbolic representation of time series, with implications for streaming algorithms
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Aurora: a new model and architecture for data stream management
The VLDB Journal — The International Journal on Very Large Data Bases
Constraint-based mining of episode rules and optimal window sizes
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
ACM SIGMOD Record
Discovering Frequent Episodes and Learning Hidden Markov Models: A Formal Connection
IEEE Transactions on Knowledge and Data Engineering
Density-based clustering for real-time stream data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Learning from Data Streams: Processing Techniques in Sensor Networks
Learning from Data Streams: Processing Techniques in Sensor Networks
The MERL motion detector dataset
Proceedings of the 2007 workshop on Massive datasets
Proceedings of the 2008 ACM symposium on Applied computing
Stream prediction using a generative model based on frequent episodes in event sequences
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge Discovery from Sensor Data
Knowledge Discovery from Sensor Data
Evaluating algorithms that learn from data streams
Proceedings of the 2009 ACM symposium on Applied Computing
Mining high utility episodes in complex event sequences
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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In this paper, we presented a frequent pattern based framework for event detection in stream data, it consists of frequent pattern discovery, frequent pattern selection and modeling three phases: In the first phase, a MNOE (Mining Non-Overlapping Episode) algorithm is proposed to find the non-overlapping frequent pattern in time series. In the frequent pattern selection phase, we proposed an EGMAMC (Episode Generated Memory Aggregation Markov Chain) model to help us selecting episodes which can describe stream data significantly. Then we defined feature flows to represent the instances of discovered frequent patterns and categorized the distribution of frequent pattern instances into three categories according to the spectrum of their feature flows. At last, we proposed a clustering algorithm EDPA (Event Detection by Pattern Aggregation) to aggregate strongly correlated frequent patterns together. We argue that strongly correlated frequent patterns form events and frequent patterns in different categories can be aggregated to form different kinds of events. Experiments on real-world sensor network datasets demonstrate that the proposed MNOE algorithm is more efficient than the existing non-overlapping episode mining algorithm and EDPA performs better when the input frequent patterns are maximal, significant and non-overlapping.