Periodicity Detection in Time Series Databases
IEEE Transactions on Knowledge and Data Engineering
Efficient mining of group patterns from user movement data
Data & Knowledge Engineering
Discovery of Periodic Patterns in Spatiotemporal Sequences
IEEE Transactions on Knowledge and Data Engineering
Unsupervised pattern mining from symbolic temporal data
ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
On Mining Movement Pattern from Mobile Users
International Journal of Distributed Sensor Networks - Heterogenous Wireless Ad Hoc and Sensor Networks
A new data structure for asynchronous periodic pattern mining
Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
Mining periodic patterns in spatio-temporal sequences at different time granularities
Intelligent Data Analysis
Pattern Mining in Discrete Time Series and Application to Music Mining
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
Mining periodic behaviors for moving objects
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
On mining 2 step walking pattern from mobile users
ICCSA'06 Proceedings of the 6th international conference on Computational Science and Its Applications - Volume Part I
Mining periodic behaviors of object movements for animal and biological sustainability studies
Data Mining and Knowledge Discovery
Closeness Preference - A new interestingness measure for sequential rules mining
Knowledge-Based Systems
Periodic pattern analysis of non-uniformly sampled stock market data
Intelligent Data Analysis
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In this paper, we focus on mining periodic patterns allowing some degreeof imperfection in the form of random replacement from a perfectperiodic pattern. Information gain was proposed to identify patternswith events of vastly different occurrence frequencies and adjust forthe deviation from a pattern. However, it does not take any penaltyif there exists some gap between the pattern occurrences. In manyapplications, e.g., bio-informatics, it is important to identify subsequencesthat a pattern repeats perfectly (or near perfectly). As a solution,we extend the information gain measure to include a penaltyfor gaps between pattern occurrences. We call this measure as generalizedinformation gain. Furthermore, we want to find subsequenceS' such that for a pattern P , the generalized information gain of Pin S' is high. This is particularly useful in locating repeats in DNAsequences. In this paper, we developed an effective mining algorithm,InfoMiner+, to simultaneously mine significant patterns and the as-sociatedsubsequences.