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Mining High-utility Temporal Patterns on Time Interval–based Data

https://doi.org/10.1145/3391230
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-04921-8_4openalex-snapshot
A Survey of High Utility Sequential Pattern Mining
inferredcites →https://doi.org/10.1007/s10115-015-0914-8openalex-snapshot
On efficiently mining high utility sequential patterns
inferredcites →https://doi.org/10.1007/s10115-019-01333-6openalex-snapshot
Mining frequent and top-K High Utility Time Interval-based Events with Duration patterns
inferredcites →https://doi.org/10.1007/s10618-007-0070-1openalex-snapshot
Efficient mining of understandable patterns from multivariate interval time series
inferredcites →https://doi.org/10.1007/s10994-016-5617-1openalex-snapshot
Memory-adaptive high utility sequential pattern mining over data streams
inferredcites →https://doi.org/10.1016/j.datak.2006.10.009openalex-snapshot
ARMADA – An algorithm for discovering richer relative temporal association rules from interval-based data
inferredcites →https://doi.org/10.1016/j.datak.2007.06.009openalex-snapshot
Isolated items discarding strategy for discovering high utility itemsets
inferredcites →https://doi.org/10.1016/j.datak.2009.06.010openalex-snapshot
Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events
inferredcites →https://doi.org/10.1016/j.engappai.2018.03.021openalex-snapshot
Efficiently updating the discovered high average-utility itemsets with transaction insertion
inferredcites →https://doi.org/10.1016/j.engappai.2019.05.010openalex-snapshot
FMaxCloHUSM: An efficient algorithm for mining frequent closed and maximal high utility sequences
inferredcites →https://doi.org/10.1016/j.ins.2018.12.070openalex-snapshot
Mining local and peak high utility itemsets
inferredcites →https://doi.org/10.1016/j.ins.2019.07.005openalex-snapshot
Correlated utility-based pattern mining
inferredcites →https://doi.org/10.1016/j.ins.2019.10.033openalex-snapshot
ProUM: Projection-based utility mining on sequence data
inferredcites →https://doi.org/10.1093/jigpal/jzz068openalex-snapshot
Mining correlated high-utility itemsets using various measures
inferredcites →https://doi.org/10.1109/34.735811openalex-snapshot
Real-time American sign language recognition using desk and wearable computer based video
inferredcites →https://doi.org/10.1109/access.2017.2717438openalex-snapshot
EHAUPM: Efficient High Average-Utility Pattern Mining With Tighter Upper Bounds
inferredcites →https://doi.org/10.1109/access.2018.2801261openalex-snapshot
MEMU: More Efficient Algorithm to Mine High Average-Utility Patterns With Multiple Minimum Average-Utility Thresholds
inferredcites →https://doi.org/10.1109/access.2018.2820740openalex-snapshot
TUB-HAUPM: Tighter Upper Bound for Mining High Average-Utility Patterns
inferredcites →https://doi.org/10.1109/access.2018.2827167openalex-snapshot
Efficient High Utility Negative Sequential Patterns Mining in Smart Campus
inferredcites →https://doi.org/10.1109/bigdata.2018.8622138openalex-snapshot
Candidate List Maintenance in High Utility Sequential Pattern Mining
inferredcites →https://doi.org/10.1109/dsaa.2017.47openalex-snapshot
Subsequence Search Considering Duration and Relations of Events in Time Interval-Based Events Sequences
inferredcites →https://doi.org/10.1109/icdm.2001openalex-snapshot
Proceedings 2001 IEEE International Conference on Data Mining
inferredcites →https://doi.org/10.1109/icdm.2012.20openalex-snapshot
Direct Discovery of High Utility Itemsets without Candidate Generation
inferredcites →https://doi.org/10.1109/icsmc.1988openalex-snapshot
Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics
inferredcites →https://doi.org/10.1109/tcyb.2020.2970176openalex-snapshot
Fast Utility Mining on Sequence Data
inferredcites →https://doi.org/10.1109/tkde.2004.77openalex-snapshot
Mining sequential patterns by pattern-growth: the PrefixSpan approach
inferredcites →https://doi.org/10.1109/tkde.2007.1055openalex-snapshot
Discovering Frequent Generalized Episodes When Events Persist for Different Durations
inferredcites →https://doi.org/10.1109/tkde.2007.190613openalex-snapshot
Mining Nonambiguous Temporal Patterns for Interval-Based Events
inferredcites →https://doi.org/10.1109/tkde.2009.46openalex-snapshot
Efficient Tree Structures for High Utility Pattern Mining in Incremental Databases
inferredcites →https://doi.org/10.1109/tkde.2015.2420557openalex-snapshot
CRoM and HuspExt: Improving Efficiency of High Utility Sequential Pattern Extraction
inferredcites →https://doi.org/10.1109/tkde.2015.2454515openalex-snapshot
Mining Temporal Patterns in Time Interval-Based Data
inferredcites →https://doi.org/10.1109/tkde.2015.2510012openalex-snapshot
Mining High Utility Patterns in One Phase without Generating Candidates
inferredcites →https://doi.org/10.1137/1.9781611972771openalex-snapshot
Proceedings of the 2007 SIAM International Conference on Data Mining
inferredcites →https://doi.org/10.1145/182.358434openalex-snapshot
Maintaining knowledge about temporal intervals
inferredcites →https://doi.org/10.1145/1871437.1871448openalex-snapshot
An efficient algorithm for mining time interval-based patterns in large database
inferredcites →https://doi.org/10.1145/2020408.2020450openalex-snapshot
Activity analysis based on low sample rate smart meters
inferredcites →https://doi.org/10.1145/2700484openalex-snapshot
Significant Correlation Pattern Mining in Smart Homes
inferredcites →https://doi.org/10.1145/2983323.2983691openalex-snapshot
Incremental Mining of High Utility Sequential Patterns in Incremental Databases
inferredcites →https://doi.org/10.1145/3178114openalex-snapshot
On Incremental High Utility Sequential Pattern Mining
inferredcites →https://doi.org/10.1145/3363571openalex-snapshot
High-Utility Itemset Mining with Effective Pruning Strategies
inferredcites →https://doi.org/10.4218/etrij.10.1510.0066openalex-snapshot
A Novel Approach for Mining High-Utility Sequential Patterns in Sequence Databases

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1016/j.engappai.2023.106182openalex-snapshot
Mining periodic high-utility itemsets with both positive and negative utilities
inferred← citeshttps://doi.org/10.1016/j.ins.2024.120421openalex-snapshot
Mining frequent temporal duration-based patterns on time interval sequential database
inferred← citeshttps://doi.org/10.1109/access.2024.3357139openalex-snapshot
Efficient Frequent Chronicle Mining Algorithms: Application to Sleep Disorder
inferred← citeshttps://doi.org/10.1109/tkde.2025.3526800openalex-snapshot
Efficient Generalized Temporal Pattern Mining in Time Series Using Mutual Information
inferred← citeshttps://doi.org/10.1145/3432200openalex-snapshot
Understanding User Behavior in Car Sharing Services Through The Lens of Mobility
inferred← citeshttps://doi.org/10.14778/3494124.3494147openalex-snapshot
Efficient temporal pattern mining in big time series using mutual information
inferred← citeshttps://doi.org/10.3233/jifs-202745openalex-snapshot
A Survey of incremental high-utility pattern mining based on storage structure
inferred← citeshttps://doi.org/10.48550/arxiv.2010.03653openalex-snapshot
Efficient Temporal Pattern Mining in Big Time Series Using Mutual Information -- Full Version
inferred← citeshttps://doi.org/10.54337/aau617104352openalex-snapshot
Efficiently Mining Temporal Patterns in Time Series Using Information Theory
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