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Discovering Frequent Closed Partial Orders from Strings
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
inferredcites →https://doi.org/10.1007/3-540-45841-7_10openalex-snapshot
On the Complexity of Generating Maximal Frequent and Minimal Infrequent Sets
inferredcites →https://doi.org/10.1007/3-540-49257-7_25openalex-snapshot
Discovering Frequent Closed Itemsets for Association Rules
inferredcites →https://doi.org/10.1007/bfb0014140openalex-snapshot
Mining sequential patterns: Generalizations and performance improvements
inferredcites →https://doi.org/10.1007/bfb0101003openalex-snapshot
Mining process models from workflow logs
inferredcites →https://doi.org/10.1023/a:1007652502315openalex-snapshot
SPADE: An Efficient Algorithm for Mining Frequent Sequences
inferredcites →https://doi.org/10.1023/a:1009748302351openalex-snapshot
Discovery of Frequent Episodes in Event Sequences
inferredcites →https://doi.org/10.1023/a:1009796218281openalex-snapshot
Levelwise Search and Borders of Theories in Knowledge Discovery
inferredcites →https://doi.org/10.1089/10665270360688075openalex-snapshot
Discovering Local Structure in Gene Expression Data: The Order-Preserving Submatrix Problem
inferredcites →https://doi.org/10.1109/fskd.2008.97openalex-snapshot
Mining Weighted Closed Sequential Patterns in Large Databases
inferredcites →https://doi.org/10.1109/icde.2001.914830openalex-snapshot
PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth
inferredcites →https://doi.org/10.1109/icde.2004.1319986openalex-snapshot
BIDE: efficient mining of frequent closed sequences
inferredcites →https://doi.org/10.1109/icde.2004.1320012openalex-snapshot
An efficient algorithm for mining frequent sequences by a new strategy without support counting
inferredcites →https://doi.org/10.1109/icdm.2003.1250919openalex-snapshot
OP-cluster: clustering by tendency in high dimensional space
inferredcites →https://doi.org/10.1109/icdm.2003.1250939openalex-snapshot
TSP: mining top-K closed sequential patterns
inferredcites →https://doi.org/10.1109/icdm.2005.57openalex-snapshot
Efficiently Mining Frequent Closed Partial Orders
inferredcites →https://doi.org/10.1109/tkde.2004.47openalex-snapshot
Workflow mining: discovering process models from event logs
inferredcites →https://doi.org/10.1109/tkde.2005.60openalex-snapshot
Efficient algorithms for mining closed itemsets and their lattice structure
inferredcites →https://doi.org/10.1137/1.9781611972726.27openalex-snapshot
CHARM: An Efficient Algorithm for Closed Itemset Mining
inferredcites →https://doi.org/10.1137/1.9781611972733.15openalex-snapshot
CloSpan: Mining: Closed Sequential Patterns in Large Datasets
inferredcites →https://doi.org/10.1137/1.9781611972733.36openalex-snapshot
ApproxMAP: Approximate Mining of Consensus Sequential Patterns
inferredcites →https://doi.org/10.1137/1.9781611972757.34openalex-snapshot
Summarizing Sequential Data with Closed Partial Orders
inferredcites →https://doi.org/10.1145/1014052.1014091openalex-snapshot
The complexity of mining maximal frequent itemsets and maximal frequent patterns
inferredcites →https://doi.org/10.1145/170035.170072openalex-snapshot
Mining association rules between sets of items in large databases
inferredcites →https://doi.org/10.1145/312129.312191openalex-snapshot
Efficient mining of emerging patterns
inferredcites →https://doi.org/10.1145/347090.347122openalex-snapshot
Global partial orders from sequential data
inferredcites →https://doi.org/10.1145/564691.564738openalex-snapshot
Mining long sequential patterns in a noisy environment
inferredcites →https://doi.org/10.1145/565196.565203openalex-snapshot
Discovering local structure in gene expression data
inferredcites →https://doi.org/10.1145/584792.584799openalex-snapshot
Mining sequential patterns with constraints in large databases
inferredcites →https://doi.org/10.1145/775047.775109openalex-snapshot
Sequential PAttern mining using a bitmap representation
inferredcites →https://doi.org/10.1145/777943.777945openalex-snapshot
Discovering all most specific sentences
inferredcites →https://doi.org/10.1145/800135.804393openalex-snapshot
The recognition of Series Parallel digraphs
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/sam.11199openalex-snapshot
MARBLES: Mining association rules buried in long event sequences
inferred← citeshttps://doi.org/10.1007/978-0-387-39940-9_172openalex-snapshot
Frequent Partial Orders
inferred← citeshttps://doi.org/10.1007/978-1-4614-1894-8openalex-snapshot
Recommender Systems for Social Tagging Systems
inferred← citeshttps://doi.org/10.1007/978-1-4614-8265-9_172openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-1-4899-7993-3_172-2openalex-snapshot
Frequent Partial Orders
inferred← citeshttps://doi.org/10.1007/978-3-030-63139-0_4openalex-snapshot
Analyzing Events and Alarms in Control Systems
inferred← citeshttps://doi.org/10.1007/978-3-030-66288-2_1openalex-snapshot
A Survey of Machine Learning for Network Fault Management
inferred← citeshttps://doi.org/10.1007/978-3-319-05461-2_1openalex-snapshot
Introduction to Pattern Mining
inferred← citeshttps://doi.org/10.1007/978-3-319-05810-8_25openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-319-18120-2_3openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-319-25255-1_9openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-319-59271-8_9openalex-snapshot
Hierarchies of Weighted Closed Partially-Ordered Patterns for Enhancing Sequential Data Analysis
inferred← citeshttps://doi.org/10.1007/978-3-319-91452-7_43openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-540-88564-1_17openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-642-01307-2_18openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-642-04747-3_13openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-642-14888-0_26openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/978-3-642-23780-5_49openalex-snapshot
Mining Actionable Partial Orders in Collections of Sequences
inferred← citeshttps://doi.org/10.1007/978-3-642-41398-8_17openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10115-010-0329-5openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10115-018-1214-xopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10489-019-01492-7openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10618-007-0070-1openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10618-011-0232-zopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10618-011-0233-yopenalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10618-013-0327-9openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10618-015-0419-9openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s41060-024-00659-xopenalex-snapshot
Sequential pattern mining algorithms and their applications: a technical review
inferred← citeshttps://doi.org/10.1016/j.artmed.2013.03.006openalex-snapshot
Discovering metric temporal constraint networks on temporal databases
inferred← citeshttps://doi.org/10.1016/j.dam.2019.02.037openalex-snapshot
RCA-Seq: An original approach for enhancing the analysis of sequential data based on hierarchies of multilevel closed partially-ordered patterns
inferred← citeshttps://doi.org/10.1016/j.datak.2009.04.011openalex-snapshot
An approach for temporal analysis of email data based on segmentation
inferred← citeshttps://doi.org/10.1016/j.ins.2019.07.098openalex-snapshot
Counting the frequency of time-constrained serial episodes in a streaming sequence
inferred← citeshttps://doi.org/10.1016/j.knosys.2012.12.013openalex-snapshot
Interrelation analysis of celestial spectra data using constrained frequent pattern trees
inferred← citeshttps://doi.org/10.1016/j.knosys.2014.12.027openalex-snapshot
Mining closed partially ordered patterns, a new optimized algorithm
inferred← citeshttps://doi.org/10.1017/9781108683173openalex-snapshot
Computational Approaches to the Network Science of Teams
inferred← citeshttps://doi.org/10.1080/03772063.2021.2021815openalex-snapshot
Natural Exponent Inertia Weight based Particle Swarm Optimization for Mining Serial Episode Rules from Event Sequences
inferred← citeshttps://doi.org/10.1109/coase.2019.8843098openalex-snapshot
Data Mining Methods to Analyze Alarm Logs in IoT Process Control Systems
inferred← citeshttps://doi.org/10.1109/icdm.2011.122openalex-snapshot
Ranking Web-Based Partial Orders by Significance Using a Markov Reference Model
inferred← citeshttps://doi.org/10.1109/icdmw.2008.82openalex-snapshot
An Efficient Sequential Pattern Mining Algorithm Based on the 2-Sequence Matrix
inferred← citeshttps://doi.org/10.1109/icdmw.2017.160openalex-snapshot
Uncovering Teamwork in Networks — Prediction, Optimization and Explanation
inferred← citeshttps://doi.org/10.1109/ichi61247.2024.00011openalex-snapshot
Sequence-Walking Decision Tree for Multivariate Healthcare Data
inferred← citeshttps://doi.org/10.1109/ictai52525.2021.00042openalex-snapshot
Unsupervised Constraint Acquisition
inferred← citeshttps://doi.org/10.1109/ipdps.2008.4536384openalex-snapshot
Improving software reliability and productivity via mining program source code
inferred← citeshttps://doi.org/10.1109/sp40001.2021.00090openalex-snapshot
ARBITRAR: User-Guided API Misuse Detection
inferred← citeshttps://doi.org/10.1109/tkde.2009.212openalex-snapshot
Signaling Potential Adverse Drug Reactions from Administrative Health Databases
inferred← citeshttps://doi.org/10.1109/tkde.2011.146openalex-snapshot
Cascading Spatio-Temporal Pattern Discovery
inferred← citeshttps://doi.org/10.1109/tkde.2017.2773493openalex-snapshot
Mining Precise-Positioning Episode Rules from Event Sequences
inferred← citeshttps://doi.org/10.1137/1.9781611972801.28openalex-snapshot
Robust mining of time intervals with semi-interval partial order patterns
inferred← citeshttps://doi.org/10.1137/1.9781611972801.29openalex-snapshot
Cascading spatio-temporal pattern discovery: A summary of results
inferred← citeshttps://doi.org/10.1137/1.9781611972825.22openalex-snapshot
MARBLES: Mining Association Rules Buried in Long Event Sequences
inferred← citeshttps://doi.org/10.1137/1.9781611974973openalex-snapshot
Proceedings of the 2017 SIAM International Conference on Data Mining
inferred← citeshttps://doi.org/10.1142/s1793830913500304openalex-snapshot
MINING POSETS FROM LINEAR ORDERS
inferred← citeshttps://doi.org/10.1145/1287624.1287630openalex-snapshot
Mining API patterns as partial orders from source code
inferred← citeshttps://doi.org/10.1145/1294301.1294302openalex-snapshot
Unsupervised pattern mining from symbolic temporal data
inferred← citeshttps://doi.org/10.1145/1816112.1816119openalex-snapshot
Margin-closed frequent sequential pattern mining
inferred← citeshttps://doi.org/10.1145/2020408.2020589openalex-snapshot
Mining closed episodes with simultaneous events
inferred← citeshttps://doi.org/10.1145/2339530.2339758openalex-snapshot
On nested palindromes in clickstream data
inferred← citeshttps://doi.org/10.1145/2431211.2431218openalex-snapshot
Sequential pattern mining -- approaches and algorithms
inferred← citeshttps://doi.org/10.1145/2566486.2568044openalex-snapshot
Finding progression stages in time-evolving event sequences
inferred← citeshttps://doi.org/10.1145/3634737.3637650openalex-snapshot
Fuzzing API Error Handling Behaviors using Coverage Guided Fault Injection
inferred← citeshttps://doi.org/10.2197/ipsjtrans.3.1openalex-snapshot
An Efficient Depth-first Search Algorithm for Extracting Frequent Diamond Episodes from Event Sequences
inferred← citeshttps://doi.org/10.4018/978-1-60566-758-4.ch013openalex-snapshot
Mining Past-Time Temporal Rules
inferred← citeshttps://doi.org/10.4028/www.scientific.net/amr.846-847.1304openalex-snapshot
Frequent Closed Partial Orders Mining in Sequences
inferred← citeshttps://doi.org/10.48550/arxiv.0902.1227openalex-snapshot
Discovering general partial orders in event streams
inferred← citeshttps://doi.org/10.48550/arxiv.1409.5512openalex-snapshot
Replacing the Irreplaceable: Fast Algorithms for Team Member Recommendation
inferred← citeshttps://doi.org/10.48550/arxiv.1701.08096openalex-snapshot
Efficiently Summarising Event Sequences with Rich Interleaving Patterns
inferred← citeshttps://doi.org/10.48550/arxiv.1712.04159openalex-snapshot
Mining Non-Redundant Local Process Models From Sequence Databases
inferred← citeshttps://doi.org/10.48550/arxiv.1801.09639openalex-snapshot
ONCE and ONCE+: Counting the Frequency of Time-constrained Serial Episodes in a Streaming Sequence
inferred← citeshttps://doi.org/10.48550/arxiv.1902.02834openalex-snapshot
The Long and the Short of It: Summarising Event Sequences with Serial Episodes
inferred← citeshttps://doi.org/10.48550/arxiv.1909.01421openalex-snapshot
Mining Insights from Weakly-Structured Event Data
inferred← citeshttps://doi.org/10.70675/0e9039b1zeed7z4a5cz8aa5z564571c1f86dopenalex-snapshot
Apprentissage de règles associatives temporelles pour les séquences temporelles de symboles
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