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Mining the Local Dependency Itemset in a Products Network

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What this work says

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

inferredcites →https://doi.org/10.1002/9781119792529.ch1openalex-snapshot
Introduction to Data Mining
inferredcites →https://doi.org/10.1002/widm.1207openalex-snapshot
A survey of itemset mining
inferredcites →https://doi.org/10.1007/3-540-45465-9_6openalex-snapshot
Local and Global Methods in Data Mining: Basic Techniques and Open Problems
inferredcites →https://doi.org/10.1007/978-1-4899-7993-3openalex-snapshot
Encyclopedia of Database Systems
inferredcites →https://doi.org/10.1007/978-3-642-27848-8_722-1openalex-snapshot
Frequent Pattern Mining
inferredcites →https://doi.org/10.1007/b137601openalex-snapshot
Local Pattern Detection: International Seminar Dagstuhl Castle, Germany, April 12-16, 2004, Revised Selected Papers
inferredcites →https://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
inferredcites →https://doi.org/10.1007/s10489-015-0750-2openalex-snapshot
Efficient mining of high utility pattern with considering of rarity and length
inferredcites →https://doi.org/10.1007/s10618-015-0403-4openalex-snapshot
Exceptional Model Mining
inferredcites →https://doi.org/10.1007/s11390-012-1302-4openalex-snapshot
Local Community Detection Using Link Similarity
inferredcites →https://doi.org/10.1007/s11573-016-0822-8openalex-snapshot
Visualizing association rules in hierarchical groups
inferredcites →https://doi.org/10.1007/s13278-010-0003-7openalex-snapshot
Market basket analysis with networks
inferredcites →https://doi.org/10.1016/j.artint.2011.05.002openalex-snapshot
Itemset mining: A constraint programming perspective
inferredcites →https://doi.org/10.1016/j.eswa.2016.03.001openalex-snapshot
High utility pattern mining over data streams with sliding window technique
inferredcites →https://doi.org/10.1016/j.eswa.2017.05.010openalex-snapshot
Utilizing advances in correlation analysis for community structure detection
inferredcites →https://doi.org/10.1016/j.eswa.2018.03.041openalex-snapshot
negFIN: An efficient algorithm for fast mining frequent itemsets
inferredcites →https://doi.org/10.1016/j.knosys.2017.03.016openalex-snapshot
An efficient algorithm for mining high utility patterns from incremental databases with one database scan
inferredcites →https://doi.org/10.1016/j.knosys.2017.12.029openalex-snapshot
Damped window based high average utility pattern mining over data streams
inferredcites →https://doi.org/10.1016/s0969-6989(02)00036-xopenalex-snapshot
Card loyalty. A new emerging issue in grocery retailing
inferredcites →https://doi.org/10.1023/a:1009713703947openalex-snapshot
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
inferredcites →https://doi.org/10.1057/dbm.2012.17openalex-snapshot
Data mining for the online retail industry: A case study of RFM model-based customer segmentation using data mining
inferredcites →https://doi.org/10.1088/1742-5468/2008/05/p05001openalex-snapshot
Evaluating local community methods in networks
inferredcites →https://doi.org/10.1088/1742-5468/2008/10/p10008openalex-snapshot
Fast unfolding of communities in large networks
inferredcites →https://doi.org/10.1103/physreve.69.026113openalex-snapshot
Finding and evaluating community structure in networks
inferredcites →https://doi.org/10.1103/physreve.72.026132openalex-snapshot
Finding local community structure in networks
inferredcites →https://doi.org/10.1109/icdis.2018.00023openalex-snapshot
Mining Dependent Items
inferredcites →https://doi.org/10.1109/icdm.2003.1250982openalex-snapshot
CoMine: efficient mining of correlated patterns
inferredcites →https://doi.org/10.1109/tcst.2017.2695169openalex-snapshot
Discovering Association Rules of Mode-Dependent Alarms From Alarm and Event Logs
inferredcites →https://doi.org/10.1109/tevc.2013.2285016openalex-snapshot
A New Multiobjective Evolutionary Algorithm for Mining a Reduced Set of Interesting Positive and Negative Quantitative Association Rules
inferredcites →https://doi.org/10.1109/tie.2017.2682782openalex-snapshot
Efficient High Utility Pattern Mining for Establishing Manufacturing Plans With Sliding Window Control
inferredcites →https://doi.org/10.1109/tkde.2012.59openalex-snapshot
Efficient Algorithms for Mining High Utility Itemsets from Transactional Databases
inferredcites →https://doi.org/10.1109/tkde.2013.41openalex-snapshot
Secure Mining of Association Rules in Horizontally Distributed Databases
inferredcites →https://doi.org/10.1109/tkde.2018.2791602openalex-snapshot
Paradoxical Correlation Pattern Mining
inferredcites →https://doi.org/10.1109/tsmcb.2008.927730openalex-snapshot
PARM—An Efficient Algorithm to Mine Association Rules From Spatial Data
inferredcites →https://doi.org/10.1145/1010614.1010616openalex-snapshot
Efficient mining of both positive and negative association rules
inferredcites →https://doi.org/10.1145/1132960.1132963openalex-snapshot
Interestingness measures for data mining
inferredcites →https://doi.org/10.1145/2396761.2396773openalex-snapshot
Mining high utility itemsets without candidate generation
inferredcites →https://doi.org/10.1145/253260.253327openalex-snapshot
Beyond market baskets
inferredcites →https://doi.org/10.1145/2623330.2623629openalex-snapshot
Community detection in graphs through correlation
inferredcites →https://doi.org/10.1145/276304.276307openalex-snapshot
Exploratory mining and pruning optimizations of constrained associations rules
inferredcites →https://doi.org/10.1145/276305.276307openalex-snapshot
Exploratory mining and pruning optimizations of constrained associations rules
inferredcites →https://doi.org/10.1145/3086188openalex-snapshot
Apriori Rule--Based In-App Ad Selection Online Algorithm for Improving Supply-Side Platform Revenues
inferredcites →https://doi.org/10.1145/342009.335372openalex-snapshot
Mining frequent patterns without candidate generation
inferredcites →https://doi.org/10.1145/958942.958944openalex-snapshot
Efficient dynamic mining of constrained frequent sets
inferredcites →https://doi.org/10.3233/ida-160861openalex-snapshot
Mining high utility itemsets based on the time decaying model

What points at this work

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

inferred← citeshttps://doi.org/10.1007/978-3-030-93733-1_6openalex-snapshot
Metamorphic Malware Behavior Analysis Using Sequential Pattern Mining
inferred← citeshttps://doi.org/10.1007/s10489-023-04618-0openalex-snapshot
Using alignment-free and pattern mining methods for SARS-CoV-2 genome analysis
inferred← citeshttps://doi.org/10.1016/j.asoc.2025.114314openalex-snapshot
HMP: Efficient heuristic algorithms for MDL-based itemset mining
inferred← citeshttps://doi.org/10.1016/j.compind.2024.104106openalex-snapshot
Analysis and classification of employee attrition and absenteeism in industry: A sequential pattern mining-based methodology
inferred← citeshttps://doi.org/10.1109/access.2023.3325839openalex-snapshot
k -PFPMiner: Top-k Periodic Frequent Patterns in Big Temporal Databases
inferred← citeshttps://doi.org/10.1109/icdis55630.2022.00057openalex-snapshot
Cluster Affiliation Model for Local Community Detection
inferred← citeshttps://doi.org/10.1109/icic64337.2024.10956625openalex-snapshot
Towards Modeling and Analyzing Transaction Data Stored as Directed Big Graphs in NoSQL DBMS for Products Recommendation
inferred← citeshttps://doi.org/10.1109/tcss.2021.3076469openalex-snapshot
On Followers Search
inferred← citeshttps://doi.org/10.1109/tkde.2023.3290095openalex-snapshot
Semi-Supervised Local Community Detection
inferred← citeshttps://doi.org/10.1111/exsy.70033openalex-snapshot
GRIMP : A Genetic Algorithm for Compression‐Based Descriptive Pattern Mining
inferred← citeshttps://doi.org/10.1145/3462636openalex-snapshot
Mining High Utility Itemsets with Hill Climbing and Simulated Annealing
inferred← citeshttps://doi.org/10.1145/3495213openalex-snapshot
An Evolutive Frequent Pattern Tree-based Incremental Knowledge Discovery Algorithm
inferred← citeshttps://doi.org/10.26599/bdma.2024.9020015openalex-snapshot
Analysis and Classification of Fake News Using Sequential Pattern Mining
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