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Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-540-68125-0_50openalex-snapshot
Efficient Mining of High Utility Itemsets from Large Datasets
inferredcites →https://doi.org/10.1007/978-3-540-74553-2_26openalex-snapshot
Mining High Utility Quantitative Association Rules
inferredcites →https://doi.org/10.1007/978-3-642-01307-2_23openalex-snapshot
The Studies of Mining Frequent Patterns Based on Frequent Pattern Tree
inferredcites →https://doi.org/10.1007/978-3-642-20149-3_18openalex-snapshot
Mining High Utility Mobile Sequential Patterns in Mobile Commerce Environments
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.knosys.2008.03.059openalex-snapshot
An efficient mining of weighted frequent patterns with length decreasing support constraints
inferredcites →https://doi.org/10.1016/j.knosys.2010.03.003openalex-snapshot
Mining weighted sequential patterns in a sequence database with a time-interval weight
inferredcites →https://doi.org/10.1080/07408170600897460openalex-snapshot
H-Mine: Fast and space-preserving frequent pattern mining in large databases
inferredcites →https://doi.org/10.1093/bioinformatics/19.1.79openalex-snapshot
Mining gene expression databases for association rules
inferredcites →https://doi.org/10.1093/bioinformatics/bti1121openalex-snapshot
Analyzing microarray data using quantitative association rules
inferredcites →https://doi.org/10.1093/bioinformatics/btn490openalex-snapshot
GenMiner: mining non-redundant association rules from integrated gene expression data and annotations
inferredcites →https://doi.org/10.1109/69.683753openalex-snapshot
Efficient data mining for path traversal patterns
inferredcites →https://doi.org/10.1109/69.846291openalex-snapshot
Scalable algorithms for association mining
inferredcites →https://doi.org/10.1109/cmpsac.2000.884697openalex-snapshot
Using pattern-join and purchase-combination for mining Web transaction patterns in an electronic commerce environment
inferredcites →https://doi.org/10.1109/icde.1999.754913openalex-snapshot
Efficient mining of partial periodic patterns in time series database
inferredcites →https://doi.org/10.1109/icdm.2003.1250893openalex-snapshot
Mining high utility itemsets
inferredcites →https://doi.org/10.1109/icdm.2008.107openalex-snapshot
Fast and Memory Efficient Mining of High Utility Itemsets in Data Streams
inferredcites →https://doi.org/10.1109/ideas.1998.694360openalex-snapshot
Mining association rules with weighted items
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.2007.190723openalex-snapshot
Mining Weighted Association Rules without Preassigned Weights
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/tsmcc.2005.855504openalex-snapshot
Mining Mobile Sequential Patterns in a Mobile Commerce Environment
inferredcites →https://doi.org/10.1137/1.9781611972757.7openalex-snapshot
Mining Frequent Itemsets from Data Streams with a Time-Sensitive Sliding Window
inferredcites →https://doi.org/10.1137/1.9781611972757.76openalex-snapshot
WFIM: Weighted Frequent Itemset Mining with a weight range and a minimum weight
inferredcites →https://doi.org/10.1137/1.9781611972764.73openalex-snapshot
WIP: mining Weighted Interesting Patterns with a strong weight and/or support affinity
inferredcites →https://doi.org/10.1145/1089827.1089839openalex-snapshot
A fast high utility itemsets mining algorithm
inferredcites →https://doi.org/10.1145/1458082.1458326openalex-snapshot
Efficient frequent pattern mining over data streams
inferredcites →https://doi.org/10.1145/1774088.1774436openalex-snapshot
Online mining of temporal maximal utility itemsets from data streams
inferredcites →https://doi.org/10.1145/335191.335372openalex-snapshot
Mining frequent patterns without candidate generation
inferredcites →https://doi.org/10.1145/342009.335372openalex-snapshot
Mining frequent patterns without candidate generation
inferredcites →https://doi.org/10.1145/347090.347149openalex-snapshot
Efficient mining of weighted association rules (WAR)
inferredcites →https://doi.org/10.1145/956750.956836openalex-snapshot
Weighted Association Rule Mining using weighted support and significance framework
inferredcites →https://doi.org/10.1145/956804.956836openalex-snapshot
Weighted Association Rule Mining using weighted support and significance framework
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/cpe.7192openalex-snapshot
Mining high utility itemsets with time‐aware scheduling using Apache Spark
inferred← citeshttps://doi.org/10.1002/int.22528openalex-snapshot
Average utility driven data analytics on damped windows for intelligent systems with data streams
inferred← citeshttps://doi.org/10.1002/widm.1242openalex-snapshot
A survey of incremental high‐utility itemset mining
inferred← citeshttps://doi.org/10.1007/978-3-030-03146-6_64openalex-snapshot
High On-Shelf Utility Mining Using an Improved HOUI-Mine Algorithm
inferred← citeshttps://doi.org/10.1007/978-3-030-04921-8_1openalex-snapshot
A Survey of High Utility Itemset Mining
inferred← citeshttps://doi.org/10.1007/978-3-030-04921-8_11openalex-snapshot
Mining Compact High Utility Itemsets Without Candidate Generation
inferred← citeshttps://doi.org/10.1007/978-3-030-04921-8_2openalex-snapshot
A Comparative Study of Top-K High Utility Itemset Mining Methods
inferred← citeshttps://doi.org/10.1007/978-3-030-04921-8_5openalex-snapshot
Efficient Algorithms for High Utility Itemset Mining Without Candidate Generation
inferred← citeshttps://doi.org/10.1007/978-3-030-04921-8_6openalex-snapshot
High Utility Association Rule Mining
inferred← citeshttps://doi.org/10.1007/978-3-030-04921-8_7openalex-snapshot
Mining High-Utility Irregular Itemsets
inferred← citeshttps://doi.org/10.1007/978-3-030-04921-8_8openalex-snapshot
A Survey of Privacy Preserving Utility Mining
inferred← citeshttps://doi.org/10.1007/978-3-030-04921-8_9openalex-snapshot
Extracting Potentially High Profit Product Feature Groups by Using High Utility Pattern Mining and Aspect Based Sentiment Analysis
inferred← citeshttps://doi.org/10.1007/978-3-030-05090-0_33openalex-snapshot
Discovering High Utility Change Points in Customer Transaction Data
inferred← citeshttps://doi.org/10.1007/978-3-030-16145-3_15openalex-snapshot
Efficiently Finding High Utility-Frequent Itemsets Using Cutoff and Suffix Utility
inferred← citeshttps://doi.org/10.1007/978-3-030-16145-3_20openalex-snapshot
Parallel Mining of Top-k High Utility Itemsets in Spark In-Memory Computing Architecture
inferred← citeshttps://doi.org/10.1007/978-3-030-27618-8_26openalex-snapshot
Discovering Partial Periodic High Utility Itemsets in Temporal Databases
inferred← citeshttps://doi.org/10.1007/978-3-030-37051-0_46openalex-snapshot
An Efficient High Utility Pattern Mining for Finding Time Based Customer Purchase Behavior
inferred← citeshttps://doi.org/10.1007/978-3-030-37188-3_17openalex-snapshot
Discovering Spatial High Utility Frequent Itemsets in Spatiotemporal Databases
inferred← citeshttps://doi.org/10.1007/978-3-030-55789-8_75openalex-snapshot
Maintenance of Prelarge High Average-Utility Patterns in Incremental Databases
inferred← citeshttps://doi.org/10.1007/978-3-030-59065-9_8openalex-snapshot
A SAT-Based Approach for Mining High Utility Itemsets from Transaction Databases
inferred← citeshttps://doi.org/10.1007/978-3-030-72139-8_1openalex-snapshot
Analytics of Multiple-Threshold Model for High Average-Utilization Patterns in Smart City Environments
inferred← citeshttps://doi.org/10.1007/978-3-030-91167-6_9openalex-snapshot
An Answer Set Programming Based Framework for High-Utility Pattern Mining Extended with Facets and Advanced Utility Functions
inferred← citeshttps://doi.org/10.1007/978-3-030-91244-4_22openalex-snapshot
Positive Correlation Based Efficient High Utility Pattern Mining Approach
inferred← citeshttps://doi.org/10.1007/978-3-030-95408-6_1openalex-snapshot
SMIM Framework to Generalize High-Utility Itemset Mining
inferred← citeshttps://doi.org/10.1007/978-3-031-11217-1_1openalex-snapshot
An Algorithm for Mining Fixed-Length High Utility Itemsets
inferred← citeshttps://doi.org/10.1007/978-3-031-24094-2_15openalex-snapshot
Hui2Vec: Learning Transaction Embedding Through High Utility Itemsets
inferred← citeshttps://doi.org/10.1007/978-3-031-42508-0_28openalex-snapshot
Mining Correlated High-Utility Itemsets Using the Cosine Measure
inferred← citeshttps://doi.org/10.1007/978-3-031-46781-3_23openalex-snapshot
MFG-HUI: An Efficient Algorithm for Mining Frequent Generators of High Utility Itemsets
inferred← citeshttps://doi.org/10.1007/978-3-031-92545-0_29openalex-snapshot
An Effective Technique for Hiding Sensitive High-Average Utility Pattern in Transaction Databases
inferred← citeshttps://doi.org/10.1007/978-3-032-01922-6_17openalex-snapshot
Conciseness Effect on High Utility Itemsets Mining
inferred← citeshttps://doi.org/10.1007/978-3-032-16632-6_26openalex-snapshot
High Utility Itemset Mining Using Roaring Bitmaps and Ant Colony Optimization
inferred← citeshttps://doi.org/10.1007/978-3-032-21822-3_20openalex-snapshot
Improving Multilevel CLOSED High-Utility Pattern Mining with Deep Learning Techniques
inferred← citeshttps://doi.org/10.1007/978-3-319-08326-1_9openalex-snapshot
FHM: Faster High-Utility Itemset Mining Using Estimated Utility Co-occurrence Pruning
inferred← citeshttps://doi.org/10.1007/978-3-319-11218-3_37openalex-snapshot
Time-Efficient Tree-Based Algorithm for Mining High Utility Patterns
inferred← citeshttps://doi.org/10.1007/978-3-319-14717-8_2openalex-snapshot
FHN: Efficient Mining of High-Utility Itemsets with Negative Unit Profits
inferred← citeshttps://doi.org/10.1007/978-3-319-14717-8_3openalex-snapshot
Novel Concise Representations of High Utility Itemsets Using Generator Patterns
inferred← citeshttps://doi.org/10.1007/978-3-319-14717-8_4openalex-snapshot
Incrementally Updating High-Utility Itemsets with Transaction Insertion
inferred← citeshttps://doi.org/10.1007/978-3-319-16313-0_3openalex-snapshot
High Utility Rare Itemset Mining over Transaction Databases
inferred← citeshttps://doi.org/10.1007/978-3-319-18032-8_51openalex-snapshot
Mining High Utility Itemsets in Big Data
inferred← citeshttps://doi.org/10.1007/978-3-319-21024-7_11openalex-snapshot
Efficient Mining of High-Utility Sequential Rules
inferred← citeshttps://doi.org/10.1007/978-3-319-27057-9_6openalex-snapshot
An Efficient Algorithm for Mining High-Utility Itemsets with Discount Notion
inferred← citeshttps://doi.org/10.1007/978-3-319-27060-9_44openalex-snapshot
EFIM: A Highly Efficient Algorithm for High-Utility Itemset Mining
inferred← citeshttps://doi.org/10.1007/978-3-319-32034-2_5openalex-snapshot
Mining Correlated High-Utility Itemsets Using the Bond Measure
inferred← citeshttps://doi.org/10.1007/978-3-319-39937-9_2openalex-snapshot
Efficient Mining of Uncertain Data for High-Utility Itemsets
inferred← citeshttps://doi.org/10.1007/978-3-319-41561-1_6openalex-snapshot
PHM: Mining Periodic High-Utility Itemsets
inferred← citeshttps://doi.org/10.1007/978-3-319-41576-5_2openalex-snapshot
The Study of the Compatibility Rules of Traditional Chinese Medicine Based on Apriori and HMETIS Hypergraph Partitioning Algorithm
inferred← citeshttps://doi.org/10.1007/978-3-319-41920-6_15openalex-snapshot
EFIM-Closed: Fast and Memory Efficient Discovery of Closed High-Utility Itemsets
inferred← citeshttps://doi.org/10.1007/978-3-319-42007-3_11openalex-snapshot
FHM $$+$$ : Faster High-Utility Itemset Mining Using Length Upper-Bound Reduction
inferred← citeshttps://doi.org/10.1007/978-3-319-43946-4_1openalex-snapshot
Mining Recent High-Utility Patterns from Temporal Databases with Time-Sensitive Constraint
inferred← citeshttps://doi.org/10.1007/978-3-319-44403-1_5openalex-snapshot
More Efficient Algorithms for Mining High-Utility Itemsets with Multiple Minimum Utility Thresholds
inferred← citeshttps://doi.org/10.1007/978-3-319-44403-1_6openalex-snapshot
Mining Minimal High-Utility Itemsets
inferred← citeshttps://doi.org/10.1007/978-3-319-46257-8_64openalex-snapshot
An Efficient Mining Algorithm for Maximal Weighted Frequent Patterns Based on WIdT-Trees
inferred← citeshttps://doi.org/10.1007/978-3-319-48490-7_16openalex-snapshot
A More Efficient Algorithm to Mine Skyline Frequent-Utility Patterns
inferred← citeshttps://doi.org/10.1007/978-3-319-50137-6_2openalex-snapshot
Data Mining and Constraints: An Overview
inferred← citeshttps://doi.org/10.1007/978-3-319-53474-9_7openalex-snapshot
An Efficient Approach for Mining High Utility Itemsets Over Data Streams
inferred← citeshttps://doi.org/10.1007/978-3-319-54472-4_51openalex-snapshot
Mining Periodic High Utility Sequential Patterns
inferred← citeshttps://doi.org/10.1007/978-3-319-57454-7_34openalex-snapshot
Mining High-Utility Itemsets with Both Positive and Negative Unit Profits from Uncertain Databases
inferred← citeshttps://doi.org/10.1007/978-3-319-57454-7_9openalex-snapshot
Purchase Signatures of Retail Customers
inferred← citeshttps://doi.org/10.1007/978-3-319-57529-2_16openalex-snapshot
mHUIMiner: A Fast High Utility Itemset Mining Algorithm for Sparse Datasets
inferred← citeshttps://doi.org/10.1007/978-3-319-63579-8_19openalex-snapshot
Exploiting High Utility Occupancy Patterns
inferred← citeshttps://doi.org/10.1007/978-3-319-64283-3_32openalex-snapshot
Extracting Non-redundant Correlated Purchase Behaviors by Utility Measure
inferred← citeshttps://doi.org/10.1007/978-3-319-67162-8_22openalex-snapshot
Discovering High-Utility Itemsets at Multiple Abstraction Levels
inferred← citeshttps://doi.org/10.1007/978-3-319-93040-4_1openalex-snapshot
Discovering High Utility Itemsets Based on the Artificial Bee Colony Algorithm
inferred← citeshttps://doi.org/10.1007/978-3-319-98812-2_41openalex-snapshot
Mining Local High Utility Itemsets
inferred← citeshttps://doi.org/10.1007/978-3-319-98812-2_42openalex-snapshot
Mining Trending High Utility Itemsets from Temporal Transaction Databases
inferred← citeshttps://doi.org/10.1007/978-3-319-99996-8_26openalex-snapshot
A Parallel Algorithm for Mining High Utility Itemsets
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A Swarm-Based Approach to Mine High-Utility Itemsets
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Mining Discriminative High Utility Patterns
inferred← citeshttps://doi.org/10.1007/978-981-10-3779-5_12openalex-snapshot
Privacy Preservation of Infrequent Itemsets Mining Using GA Approach
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FP-Tree and Its Variants: Towards Solving the Pattern Mining Challenges
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Efficient High-Utility Itemset Mining Over Variety of Databases: A Survey
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Review on High Utility Rare Itemset Mining
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Effective Mining of High Utility Itemsets with Automated Minimum Utility Thresholds
inferred← citeshttps://doi.org/10.1007/978-981-15-4917-5_33openalex-snapshot
Mining High-Utility Itemsets of Generalized Quantity with Pattern-Growth Structures
inferred← citeshttps://doi.org/10.1007/978-981-15-8767-2_30openalex-snapshot
High Average Utility Itemset Mining: A Survey
inferred← citeshttps://doi.org/10.1007/978-981-16-0980-0_34openalex-snapshot
High Utility Pattern Mining Distributed Algorithm Based on Spark RDD
inferred← citeshttps://doi.org/10.1007/978-981-16-3964-7_10openalex-snapshot
Hiding Periodic High-Utility Sequential Patterns
inferred← citeshttps://doi.org/10.1007/978-981-16-3964-7_11openalex-snapshot
NetHAPP: High Average Utility Periodic Gapped Sequential Pattern Mining
inferred← citeshttps://doi.org/10.1007/978-981-16-3964-7_2openalex-snapshot
Discovering Frequent Patterns in Very Large Transactional Databases
inferred← citeshttps://doi.org/10.1007/978-981-16-5640-8_34openalex-snapshot
Ensuring Privacy of Data and Mined Results of Data Possessor in Collaborative ARM
inferred← citeshttps://doi.org/10.1007/978-981-16-5655-2_16openalex-snapshot
Parallel Algorithm to Efficiently Mine High Utility Itemset
inferred← citeshttps://doi.org/10.1007/978-981-16-5689-7_18openalex-snapshot
Knowledge Discovery and Data Mining for Intelligent Business Solutions
inferred← citeshttps://doi.org/10.1007/978-981-16-7566-9_8openalex-snapshot
High Utility Co-location Patterns
inferred← citeshttps://doi.org/10.1007/978-981-19-1520-8_2openalex-snapshot
A Review of High Utility Itemset Mining for Transactional Database
inferred← citeshttps://doi.org/10.1007/978-981-92-0042-9_24openalex-snapshot
Efficient High Utility Itemset Mining on Massive Data
inferred← citeshttps://doi.org/10.1007/978-981-92-0375-8_2openalex-snapshot
Adaptive GPU Compute Resource Allocation for Efficient High-Utility Itemset Mining
inferred← citeshttps://doi.org/10.1007/s00354-019-00078-8openalex-snapshot
Mining High-Average Utility Itemsets with Positive and Negative External Utilities
inferred← citeshttps://doi.org/10.1007/s00500-016-2159-1openalex-snapshot
Efficiently mining uncertain high-utility itemsets
inferred← citeshttps://doi.org/10.1007/s00500-020-05123-zopenalex-snapshot
RETRACTED ARTICLE: High utility itemset mining: a Boolean operators-based modified grey wolf optimization algorithm
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A survey on soft computing-based high-utility itemsets mining
inferred← citeshttps://doi.org/10.1007/s00500-022-07123-7openalex-snapshot
Mining fuzzy high average-utility itemsets using fuzzy utility lists and efficient pruning approach
inferred← citeshttps://doi.org/10.1007/s00500-025-10930-3openalex-snapshot
TKHN: efficient mining of top-K high utility itemsets with negative item values
inferred← citeshttps://doi.org/10.1007/s10115-015-0900-1openalex-snapshot
A high utility itemset mining algorithm based on subsume index
inferred← citeshttps://doi.org/10.1007/s10115-016-0986-0openalex-snapshot
EFIM: a fast and memory efficient algorithm for high-utility itemset mining
inferred← citeshttps://doi.org/10.1007/s10115-016-0989-xopenalex-snapshot
Indexed list-based high utility pattern mining with utility upper-bound reduction and pattern combination techniques
inferred← citeshttps://doi.org/10.1007/s10115-016-0991-3openalex-snapshot
FDHUP: Fast algorithm for mining discriminative high utility patterns
inferred← citeshttps://doi.org/10.1007/s10115-016-1020-2openalex-snapshot
An efficient algorithm for mining top-k on-shelf high utility itemsets
inferred← citeshttps://doi.org/10.1007/s10115-018-1207-9openalex-snapshot
DMHUPS: Discovering Multiple High Utility Patterns Simultaneously
inferred← citeshttps://doi.org/10.1007/s10115-019-01385-8openalex-snapshot
High average-utility sequential pattern mining based on uncertain databases
inferred← citeshttps://doi.org/10.1007/s10115-020-01485-wopenalex-snapshot
An efficient projection-based method for high utility itemset mining using a novel pruning approach on the utility matrix
inferred← citeshttps://doi.org/10.1007/s10115-022-01763-9openalex-snapshot
An efficient algorithm for mining closed high utility itemsets over data streams with one dataset scan
inferred← citeshttps://doi.org/10.1007/s10115-023-01948-wopenalex-snapshot
A residual utility-based concept for high-utility itemset mining
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CG-FHAUI: an efficient algorithm for simultaneously mining succinct pattern sets of frequent high average utility itemsets
inferred← citeshttps://doi.org/10.1007/s10115-024-02288-zopenalex-snapshot
High utility itemset mining in data stream using elephant herding optimization
inferred← citeshttps://doi.org/10.1007/s10115-026-02776-4openalex-snapshot
Extracting interesting CoHUPs from dataset with skewed and non-skewed item’s support distribution
inferred← citeshttps://doi.org/10.1007/s10489-014-0601-6openalex-snapshot
Incremental high utility pattern mining with static and dynamic databases
inferred← citeshttps://doi.org/10.1007/s10489-014-0622-1openalex-snapshot
Associating absent frequent itemsets with infrequent items to identify abnormal transactions
inferred← citeshttps://doi.org/10.1007/s10489-015-0697-3openalex-snapshot
Maintaining the discovered high-utility itemsets with transaction modification
inferred← citeshttps://doi.org/10.1007/s10489-015-0740-4openalex-snapshot
An efficient fast algorithm for discovering closed+ high utility itemsets
inferred← citeshttps://doi.org/10.1007/s10489-016-0890-zopenalex-snapshot
Efficient algorithm for mining high average-utility itemsets in incremental transaction databases
inferred← citeshttps://doi.org/10.1007/s10489-017-0932-1openalex-snapshot
A hybrid framework for mining high-utility itemsets in a sparse transaction database
inferred← citeshttps://doi.org/10.1007/s10489-017-0939-7openalex-snapshot
Mining top-k high-utility itemsets from a data stream under sliding window model
inferred← citeshttps://doi.org/10.1007/s10489-017-1057-2openalex-snapshot
Efficient high utility itemset mining using buffered utility-lists
inferred← citeshttps://doi.org/10.1007/s10489-017-1130-xopenalex-snapshot
An efficient structure for fast mining high utility itemsets
inferred← citeshttps://doi.org/10.1007/s10489-018-1155-9openalex-snapshot
Top-k high average-utility itemsets mining with effective pruning strategies
inferred← citeshttps://doi.org/10.1007/s10489-018-1180-8openalex-snapshot
Maintenance algorithm for high average-utility itemsets with transaction deletion
inferred← citeshttps://doi.org/10.1007/s10489-018-1245-8openalex-snapshot
A new framework for metaheuristic-based frequent itemset mining
inferred← citeshttps://doi.org/10.1007/s10489-018-1316-xopenalex-snapshot
TKEH: an efficient algorithm for mining top-k high utility itemsets
inferred← citeshttps://doi.org/10.1007/s10489-020-01743-yopenalex-snapshot
Incrementally updating the high average-utility patterns with pre-large concept
inferred← citeshttps://doi.org/10.1007/s10489-020-02063-xopenalex-snapshot
High-utility and diverse itemset mining
inferred← citeshttps://doi.org/10.1007/s10489-021-02505-0openalex-snapshot
Mining interesting sequences with low average cost and high average utility
inferred← citeshttps://doi.org/10.1007/s10489-021-02539-4openalex-snapshot
Dynamic maintenance model for high average-utility pattern mining with deletion operation
inferred← citeshttps://doi.org/10.1007/s10489-021-02611-zopenalex-snapshot
High average-utility itemsets mining: a survey
inferred← citeshttps://doi.org/10.1007/s10489-021-02681-zopenalex-snapshot
An efficient method for mining multi-level high utility Itemsets
inferred← citeshttps://doi.org/10.1007/s10489-021-02751-2openalex-snapshot
Analytics of high average-utility patterns in the industrial internet of things
inferred← citeshttps://doi.org/10.1007/s10489-021-02922-1openalex-snapshot
Discovery of closed high utility itemsets using a fast nature-inspired ant colony algorithm
inferred← citeshttps://doi.org/10.1007/s10489-021-03134-3openalex-snapshot
Efficient evolutionary computation model of closed high-utility itemset mining
inferred← citeshttps://doi.org/10.1007/s10489-022-03722-xopenalex-snapshot
Efficient mining high average-utility itemsets with effective pruning strategies and novel list structure
inferred← citeshttps://doi.org/10.1007/s10489-022-03850-4openalex-snapshot
An efficient utility-list based high-utility itemset mining algorithm
inferred← citeshttps://doi.org/10.1007/s10489-022-04436-wopenalex-snapshot
HDSHUI-miner: a novel algorithm for discovering spatial high-utility itemsets in high-dimensional spatiotemporal databases
inferred← citeshttps://doi.org/10.1007/s10489-023-04554-zopenalex-snapshot
Mining frequent weighted utility patterns with dynamic weighted items from quantitative databases
inferred← citeshttps://doi.org/10.1007/s10489-023-04853-5openalex-snapshot
High utility itemsets mining from transactional databases: a survey
inferred← citeshttps://doi.org/10.1007/s10489-023-04864-2openalex-snapshot
List-based mining top-k average-utility itemsets with effective pruning and threshold raising strategies
inferred← citeshttps://doi.org/10.1007/s10489-023-04913-wopenalex-snapshot
A new algorithm using integer programming relaxation for privacy-preserving in utility mining
inferred← citeshttps://doi.org/10.1007/s10489-023-04925-6openalex-snapshot
High utility pattern mining algorithm over data streams using ext-list.
inferred← citeshttps://doi.org/10.1007/s10489-023-05076-4openalex-snapshot
Mining top-k high average-utility itemsets based on breadth-first search
inferred← citeshttps://doi.org/10.1007/s10489-023-05145-8openalex-snapshot
New approaches for mining high utility itemsets with multiple utility thresholds
inferred← citeshttps://doi.org/10.1007/s10489-024-05826-yopenalex-snapshot
Novel stochastic algorithms for privacy-preserving utility mining
inferred← citeshttps://doi.org/10.1007/s10489-025-06729-2openalex-snapshot
Efficient high utility itemsets mining over data streams with compact utility list structure
inferred← citeshttps://doi.org/10.1007/s10489-025-06969-2openalex-snapshot
Two top-k HUIM algorithms based on the particle filter theory
inferred← citeshttps://doi.org/10.1007/s10489-025-06995-0openalex-snapshot
Discovering top-k periodic and high-utility patterns
inferred← citeshttps://doi.org/10.1007/s10586-018-1959-8openalex-snapshot
Handling high web access utility mining using intelligent hybrid hill climbing algorithm based tree construction
inferred← citeshttps://doi.org/10.1007/s10586-021-03442-wopenalex-snapshot
A Spark-based high utility itemset mining with multiple external utilities
inferred← citeshttps://doi.org/10.1007/s10618-016-0480-zopenalex-snapshot
Survey on using constraints in data mining
inferred← citeshttps://doi.org/10.1007/s10618-023-00949-1openalex-snapshot
Sky-signatures: detecting and characterizing recurrent behavior in sequential data
inferred← citeshttps://doi.org/10.1007/s11042-014-2178-9openalex-snapshot
Updating high-utility pattern trees with transaction modification
inferred← citeshttps://doi.org/10.1007/s11227-020-03247-5openalex-snapshot
A fast high average-utility itemset mining with efficient tighter upper bounds and novel list structure
inferred← citeshttps://doi.org/10.1007/s11227-021-04218-0openalex-snapshot
OHUQI: Mining on-shelf high-utility quantitative itemsets
inferred← citeshttps://doi.org/10.1007/s11227-026-08247-5openalex-snapshot
Efficiently mining frequent weighted utility closed patterns with pruning strategies from dynamic quantitative databases
inferred← citeshttps://doi.org/10.1007/s11277-020-07753-wopenalex-snapshot
High Utility Itemsets Mining Based on Divide-and-Conquer Strategy
inferred← citeshttps://doi.org/10.1007/s11390-016-1662-2openalex-snapshot
HUITWU: An Efficient Algorithm for High-Utility Itemset Mining in Transaction Databases
inferred← citeshttps://doi.org/10.1007/s11704-016-6245-4openalex-snapshot
CLS-Miner: efficient and effective closed high-utility itemset mining
inferred← citeshttps://doi.org/10.1007/s12083-020-00980-9openalex-snapshot
High utility itemset mining using path encoding and constrained subset generation
inferred← citeshttps://doi.org/10.1007/s12652-020-01706-8openalex-snapshot
RETRACTED ARTICLE: An improved memory adaptive up-growth to mine high utility itemsets from large transaction databases
inferred← citeshttps://doi.org/10.1007/s13042-025-02745-3openalex-snapshot
Targeted mining of periodic and high-utility patterns
inferred← citeshttps://doi.org/10.1007/s13369-021-05936-6openalex-snapshot
RETRACTED ARTICLE: Utilizing Index-Based Periodic High Utility Mining to Study Frequent Itemsets
inferred← citeshttps://doi.org/10.1007/s13369-023-07971-xopenalex-snapshot
HAUOPM: High Average Utility Occupancy Pattern Mining
inferred← citeshttps://doi.org/10.1007/s41060-024-00538-5openalex-snapshot
An efficient join operations for utility list-based high-utility mining approaches using hybrid search technique
inferred← citeshttps://doi.org/10.1007/s42044-021-00083-5openalex-snapshot
A parallel approach for high utility-based frequent pattern mining in a big data environment
inferred← citeshttps://doi.org/10.1007/s42979-023-01967-yopenalex-snapshot
RSPHUIM: Recent Short Period High Utility Itemset Mining
inferred← citeshttps://doi.org/10.1007/s42979-025-03725-8openalex-snapshot
Crowd Distance Induced Multi Objective Binary Salp Swarm Optimization Algorithm for Mining High Frequency and Utility Itemsets
inferred← citeshttps://doi.org/10.1016/j.aei.2014.08.003openalex-snapshot
Efficient updating of discovered high-utility itemsets for transaction deletion in dynamic databases
inferred← citeshttps://doi.org/10.1016/j.aei.2015.05.003openalex-snapshot
A fast updated algorithm to maintain the discovered high-utility itemsets for transaction modification
inferred← citeshttps://doi.org/10.1016/j.aei.2016.04.002openalex-snapshot
An efficient algorithm to mine high average-utility itemsets
inferred← citeshttps://doi.org/10.1016/j.aei.2017.04.007openalex-snapshot
A two-phase approach to mine short-period high-utility itemsets in transactional databases
inferred← citeshttps://doi.org/10.1016/j.aej.2026.03.002openalex-snapshot
Exploring and detecting utility streams on prelarge and time fading properties
inferred← citeshttps://doi.org/10.1016/j.asoc.2021.107422openalex-snapshot
A predictive GA-based model for closed high-utility itemset mining
inferred← citeshttps://doi.org/10.1016/j.asoc.2022.109073openalex-snapshot
High-utility itemsets mining based on binary particle swarm optimization with multiple adjustment strategies
inferred← citeshttps://doi.org/10.1016/j.asoc.2024.112606openalex-snapshot
Efficient sampling policy to accelerate high utility pattern mining at given accuracy level
inferred← citeshttps://doi.org/10.1016/j.asoc.2025.112902openalex-snapshot
Efficient fuzzy-based high utility pattern computing and analyzing approach with temporal properties
inferred← citeshttps://doi.org/10.1016/j.bdr.2016.07.001openalex-snapshot
Approximate Parallel High Utility Itemset Mining
inferred← citeshttps://doi.org/10.1016/j.cose.2023.103360openalex-snapshot
Efficient privacy preserving algorithms for hiding sensitive high utility itemsets
inferred← citeshttps://doi.org/10.1016/j.engappai.2017.12.012openalex-snapshot
Efficient mining of high utility itemsets with multiple minimum utility thresholds
inferred← citeshttps://doi.org/10.1016/j.engappai.2018.03.021openalex-snapshot
Efficiently updating the discovered high average-utility itemsets with transaction insertion
inferred← citeshttps://doi.org/10.1016/j.engappai.2018.10.010openalex-snapshot
Mining of skyline patterns by considering both frequent and utility constraints
inferred← citeshttps://doi.org/10.1016/j.engappai.2018.12.006openalex-snapshot
A novel algorithm for privacy preserving utility mining based on integer linear programming
inferred← citeshttps://doi.org/10.1016/j.engappai.2020.103933openalex-snapshot
High average-utility itemset mining with multiple minimum utility threshold: A generalized approach
inferred← citeshttps://doi.org/10.1016/j.engappai.2021.104516openalex-snapshot
On-shelf utility mining from transaction database
inferred← citeshttps://doi.org/10.1016/j.engappai.2022.105355openalex-snapshot
Effective algorithms to mine skyline frequent-utility itemsets
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.engappai.2023.107047openalex-snapshot
Targeted mining of top- k high utility itemsets
inferred← citeshttps://doi.org/10.1016/j.engappai.2023.107111openalex-snapshot
Efficient mining of concise and informative representations of frequent high utility itemsets
inferred← citeshttps://doi.org/10.1016/j.engappai.2024.109960openalex-snapshot
Fast mining local high-utility itemsets
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.110081openalex-snapshot
Efficient mining of closed high-utility itemsets in dynamic and incremental databases
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.113288openalex-snapshot
OPHEP-Miner: One-phase high-efficiency pattern mining utilizing tree structures
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.113639openalex-snapshot
Mining high average-efficiency itemsets based on a compact list structure
inferred← citeshttps://doi.org/10.1016/j.eswa.2013.11.038openalex-snapshot
High utility itemset mining with techniques for reducing overestimated utilities and pruning candidates
inferred← citeshttps://doi.org/10.1016/j.eswa.2014.08.037openalex-snapshot
A fast perturbation algorithm using tree structure for privacy preserving utility mining
inferred← citeshttps://doi.org/10.1016/j.eswa.2014.11.001openalex-snapshot
Pruning strategies for mining high utility itemsets
inferred← citeshttps://doi.org/10.1016/j.eswa.2015.02.051openalex-snapshot
An efficient approach for mining association rules from high utility itemsets
inferred← citeshttps://doi.org/10.1016/j.eswa.2016.03.001openalex-snapshot
High utility pattern mining over data streams with sliding window technique
inferred← citeshttps://doi.org/10.1016/j.eswa.2017.05.051openalex-snapshot
Planning stock portfolios by means of weighted frequent itemsets
inferred← citeshttps://doi.org/10.1016/j.eswa.2017.08.028openalex-snapshot
HMiner: Efficiently mining high utility itemsets
inferred← citeshttps://doi.org/10.1016/j.eswa.2018.02.008openalex-snapshot
An empirical evaluation of high utility itemset mining algorithms
inferred← citeshttps://doi.org/10.1016/j.eswa.2018.03.019openalex-snapshot
A pure array structure and parallel strategy for high-utility sequential pattern mining
inferred← citeshttps://doi.org/10.1016/j.eswa.2018.09.051openalex-snapshot
Mining top-k high utility itemsets with effective threshold raising strategies
inferred← citeshttps://doi.org/10.1016/j.eswa.2019.01.079openalex-snapshot
Efficient mining of extraordinary patterns by pruning and predicting
inferred← citeshttps://doi.org/10.1016/j.eswa.2019.112967openalex-snapshot
Distributed mining of high utility time interval sequential patterns using mapreduce approach
inferred← citeshttps://doi.org/10.1016/j.eswa.2020.114276openalex-snapshot
vertTIRP: Robust and efficient vertical frequent time interval-related pattern mining
inferred← citeshttps://doi.org/10.1016/j.eswa.2020.114464openalex-snapshot
Efficient algorithms for discovering high-utility patterns with strong frequency affinities
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