Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
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
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Finding Constrained Frequent Episodes Using Minimal Occurrences
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Reliable detection of episodes in event sequences
Knowledge and Information Systems
A fast high utility itemsets mining algorithm
UBDM '05 Proceedings of the 1st international workshop on Utility-based data mining
A fast algorithm for finding frequent episodes in event streams
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient mining of frequent episodes from complex sequences
Information Systems
Isolated items discarding strategy for discovering high utility itemsets
Data & Knowledge Engineering
Fast and Memory Efficient Mining of High Utility Itemsets in Data Streams
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
A frequent pattern based framework for event detection in sensor network stream data
Proceedings of the Third International Workshop on Knowledge Discovery from Sensor Data
Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases
IEEE Transactions on Knowledge and Data Engineering
Parallel Method for Mining High Utility Itemsets from Vertically Partitioned Distributed Databases
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
Mining frequent episodes for relating financial events and stock trends
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
UP-Growth: an efficient algorithm for high utility itemset mining
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining high utility mobile sequential patterns in mobile commerce environments
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining closed episodes with simultaneous events
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Mining of a Concise and Lossless Representation of High Utility Itemsets
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Mining top-K high utility itemsets
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
The long and the short of it: summarising event sequences with serial episodes
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
USpan: an efficient algorithm for mining high utility sequential patterns
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Mining high utility itemsets without candidate generation
Proceedings of the 21st ACM international conference on Information and knowledge management
Direct Discovery of High Utility Itemsets without Candidate Generation
ICDM '12 Proceedings of the 2012 IEEE 12th International Conference on Data Mining
Finding progression stages in time-evolving event sequences
Proceedings of the 23rd international conference on World wide web
Expert Systems with Applications: An International Journal
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Frequent episode mining (FEM) is an interesting research topic in data mining with wide range of applications. However, the traditional framework of FEM treats all events as having the same importance/utility and assumes that a same type of event appears at most once at any time point. These simplifying assumptions do not reflect the characteristics of scenarios in real applications and thus the useful information of episodes in terms of utilities such as profits is lost. Furthermore, most studies on FEM focused on mining episodes in simple event sequences and few considered the scenario of complex event sequences, where different events can occur simultaneously. To address these issues, in this paper, we incorporate the concept of utility into episode mining and address a new problem of mining high utility episodes from complex event sequences, which has not been explored so far. In the proposed framework, the importance/utility of different events is considered and multiple events can appear simultaneously. Several novel features are incorporated into the proposed framework to resolve the challenges raised by this new problem, such as the absence of anti-monotone property and the huge set of candidate episodes. Moreover, an efficient algorithm named UP-Span (Utility ePisodes mining by Spanning prefixes) is proposed for mining high utility episodes with several strategies incorporated for pruning the search space to achieve high efficiency. Experimental results on real and synthetic datasets show that UP-Span has excellent performance and serves as an effective solution to the new problem of mining high utility episodes from complex event sequences.