On the public citation graph

Essential classification rule sets

https://doi.org/10.1145/1042046.1042048

0 signed · 77 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.

Claim with ORCID

Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.

See reference health

What this work says

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

inferredcites →https://doi.org/10.1006/jpdc.2000.1693openalex-snapshot

A Tree Projection Algorithm for Generation of Frequent Item Sets

inferredcites →https://doi.org/10.1007/3-540-44957-4_65openalex-snapshot

Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets

inferredcites →https://doi.org/10.1007/3-540-45422-5_24openalex-snapshot

Intelligent Structuring and Reducing of Association Rules with Formal Concept Analysis

inferredcites →https://doi.org/10.1007/3-540-45681-3_7openalex-snapshot

Mining All Non-derivable Frequent Itemsets

inferredcites →https://doi.org/10.1007/3-540-46846-3_4openalex-snapshot

CAEP: Classification by Aggregating Emerging Patterns

inferredcites →https://doi.org/10.1007/3-540-47887-6_15openalex-snapshot

Concise Representation of Frequent Patterns Based on Generalized Disjunction-Free Generators

inferredcites →https://doi.org/10.1007/3-540-49257-7_25openalex-snapshot

Discovering Frequent Closed Itemsets for Association Rules

inferredcites →https://doi.org/10.1007/978-1-4471-0651-7_3openalex-snapshot

Simplest Rules Characterizing Classes Generated by δ-Free Sets

inferredcites →https://doi.org/10.1016/b978-044451378-6/50001-6openalex-snapshot

An Introduction to Support Vector Machines

inferredcites →https://doi.org/10.1016/s0306-4379(99)00003-4openalex-snapshot

Efficient mining of association rules using closed itemset lattices

inferredcites →https://doi.org/10.1017/cbo9780511801389openalex-snapshot

An Introduction to Support Vector Machines and Other Kernel-based Learning Methods

inferredcites →https://doi.org/10.1023/a:1007608224229openalex-snapshot

A Comparison of Prediction Accuracy, Complexity, and Training Time of Thirty-Three Old and New Classification Algorithms

inferredcites →https://doi.org/10.1023/a:1009713703947openalex-snapshot

Beyond Market Baskets: Generalizing Association Rules to Dependence Rules

inferredcites →https://doi.org/10.1023/a:1021571501451openalex-snapshot

Free-Sets: A Condensed Representation of Boolean Data for the Approximation of Frequency Queries

inferredcites →https://doi.org/10.1108/ir.1999.04926fae.001openalex-snapshot

Lecture Notes in Computer Science 1205

inferredcites →https://doi.org/10.1109/69.250074openalex-snapshot

Database mining: a performance perspective

inferredcites →https://doi.org/10.1109/icde.2001.914857openalex-snapshot

MAFIA: a maximal frequent itemset algorithm for transactional databases

inferredcites →https://doi.org/10.1109/icdm.2001.989533openalex-snapshot

Concise representation of frequent patterns based on disjunction-free generators

inferredcites →https://doi.org/10.1109/icdm.2001.989541openalex-snapshot

CMAR: accurate and efficient classification based on multiple class-association rules

inferredcites →https://doi.org/10.1109/icdm.2002.1183878openalex-snapshot

Proceedings 2002 IEEE International Conference on Data Mining. ICDM 2002

inferredcites →https://doi.org/10.1109/icdm.2002.1183883openalex-snapshot

A lazy approach to pruning classification rules

inferredcites →https://doi.org/10.1109/tac.1974.1100577openalex-snapshot

Pattern classification and scene analysis

inferredcites →https://doi.org/10.1109/tpami.1980.4767034openalex-snapshot

Pattern Recognition as Rule-Guided Inductive Inference

inferredcites →https://doi.org/10.1137/1.9781611972726.27openalex-snapshot

CHARM: An Efficient Algorithm for Closed Itemset Mining

inferredcites →https://doi.org/10.1137/1.9781611972733.40openalex-snapshot

CPAR: Classification based on Predictive Association Rules

inferredcites →https://doi.org/10.1145/233269.233313openalex-snapshot

Data mining using two-dimensional optimized association rules

inferredcites →https://doi.org/10.1145/253260.253325openalex-snapshot

Dynamic itemset counting and implication rules for market basket data

inferredcites →https://doi.org/10.1145/276304.276313openalex-snapshot

Efficiently mining long patterns from databases

inferredcites →https://doi.org/10.1145/312129.312216openalex-snapshot

Pruning and summarizing the discovered associations

inferredcites →https://doi.org/10.1145/312129.312219openalex-snapshot

Mining the most interesting rules

inferredcites →https://doi.org/10.1145/335191.335372openalex-snapshot

Mining frequent patterns without candidate generation

inferredcites →https://doi.org/10.1145/335191.336572openalex-snapshot

Towards data mining benchmarking

inferredcites →https://doi.org/10.1145/342009.335372openalex-snapshot

Mining frequent patterns without candidate generation

inferredcites →https://doi.org/10.1145/347090.347101openalex-snapshot

Generating non-redundant association rules

inferredcites →https://doi.org/10.1145/347090.347147openalex-snapshot

Growing decision trees on support-less association rules

inferredcites →https://doi.org/10.1145/363951.363956openalex-snapshot

Theory of dependence values

inferredcites →https://doi.org/10.1145/375551.375604openalex-snapshot

A condensed representation to find frequent patterns

inferredcites →https://doi.org/10.1145/380995.381017openalex-snapshot

Mining frequent patterns with counting inference

inferredcites →https://doi.org/10.1145/582192openalex-snapshot

Proceedings of the 1983 ACM SIGMOD international conference on Management of data - SIGMOD '83

inferredcites →https://doi.org/10.1145/775047.775053openalex-snapshot

Selecting the right interestingness measure for association patterns

inferredcites →https://doi.org/10.1145/956750.956779openalex-snapshot

CLOSET+

inferredcites →https://doi.org/10.21236/ada164453openalex-snapshot

Learning Internal Representations by Error Propagation

What points at this work

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

inferred← citeshttps://doi.org/10.2139/ssrn.4088434openalex-extracted

Comprehensive Pedestrian Level of Service Measure For Urban Footways Using Unsupervised Mining

inferred← citeshttps://doi.org/10.5281/zenodo.1082148openalex-extracted

Association Rule And Decision Tree Based Methodsfor Fuzzy Rule Base Generation

inferred← citeshttps://doi.org/10.1007/11871637_19openalex-snapshot

Closed Sets for Labeled Data

inferred← citeshttps://doi.org/10.1007/978-0-387-39940-9_558openalex-snapshot

Classification by Association Rule Analysis

inferred← citeshttps://doi.org/10.1007/978-0-85729-525-5_10openalex-snapshot

Conclusions

inferred← citeshttps://doi.org/10.1007/978-1-4614-8265-9_558openalex-snapshot

Classification by Association Rule Analysis

inferred← citeshttps://doi.org/10.1007/978-1-4899-7993-3_558-2openalex-snapshot

Classification by Association Rule Analysis

inferred← citeshttps://doi.org/10.1007/978-3-540-68125-0_12openalex-snapshot

Feature Construction Based on Closedness Properties Is Not That Simple

inferred← citeshttps://doi.org/10.1007/978-3-540-78488-3_1openalex-snapshot

Compact Representations of Sequential Classification Rules

inferred← citeshttps://doi.org/10.1007/978-3-540-85836-2_28openalex-snapshot

A Parameter-Free Associative Classification Method

inferred← citeshttps://doi.org/10.1007/978-3-642-14467-7_13openalex-snapshot

On the Relation between Jumping Emerging Patterns and Rough Set Theory with Application to Data Classification

inferred← citeshttps://doi.org/10.1007/978-3-642-35855-5_1openalex-snapshot

A Bayesian Criterion for Evaluating the Robustness of Classification Rules in Binary Data Sets

inferred← citeshttps://doi.org/10.1007/s10115-010-0369-xopenalex-snapshot

Application-independent feature construction based on almost-closedness properties

inferred← citeshttps://doi.org/10.1016/j.asoc.2009.11.004openalex-snapshot

A fast pruning redundant rule method using Galois connection

inferred← citeshttps://doi.org/10.1016/j.datak.2009.05.001openalex-snapshot

Sweeping the disjunctive search space towards mining new exact concise representations of frequent itemsets

inferred← citeshttps://doi.org/10.1016/j.datak.2013.06.001openalex-snapshot

Parameter-free classification in multi-class imbalanced data sets

inferred← citeshttps://doi.org/10.1016/j.eswa.2008.08.046openalex-snapshot

Post-processing of associative classification rules using closed sets

inferred← citeshttps://doi.org/10.1016/j.ins.2012.08.005openalex-snapshot

Looking for a structural characterization of the sparseness measure of (frequent closed) itemset contexts

inferred← citeshttps://doi.org/10.1109/dmo.2011.5976515openalex-snapshot

Reducing network intrusion detection association rules using Chi-Squared pruning technique

inferred← citeshttps://doi.org/10.1109/ecticon.2009.5137134openalex-snapshot

DIMAR - Discovering interesting medical association rules form MRI scans

inferred← citeshttps://doi.org/10.1109/ettandgrs.2008.96openalex-snapshot

Research and Application of Association Rules Mining in Group-Based Teaching

inferred← citeshttps://doi.org/10.1109/icdim.2007.4444238openalex-snapshot

Pattern-based decision tree construction

inferred← citeshttps://doi.org/10.1109/skg.2008.16openalex-snapshot

Chinese Text Classification Based on the BVB Model

inferred← citeshttps://doi.org/10.1109/tkde.2006.129openalex-snapshot

Robust rule-based prediction

inferred← citeshttps://doi.org/10.1109/tkde.2007.190677openalex-snapshot

A Lazy Approach to Associative Classification

inferred← citeshttps://doi.org/10.1186/1687-6180-2011-62openalex-snapshot

Distance-based features in pattern classification

inferred← citeshttps://doi.org/10.1201/b12986-34openalex-snapshot

Mining Emerging Patterns for Activity Recognition

inferred← citeshttps://doi.org/10.22146/ijccs.5207openalex-snapshot

Class Association Rule Pada Metode Associative Classification

inferred← citeshttps://doi.org/10.25100/peu.47openalex-snapshot

Fundamentos de Bases de Datos - Notas de referencia

inferred← citeshttps://doi.org/10.3233/ida-2012-0540openalex-snapshot

Key roles of closed sets and minimal generators in concise representations of frequent patterns

inferred← citeshttps://doi.org/10.4018/978-1-5225-2483-0.ch006openalex-snapshot

Knowledge Discovery From Massive Data Streams

inferred← citeshttps://doi.org/10.4018/978-1-5225-5191-1.ch068openalex-snapshot

Knowledge Discovery From Massive Data Streams

inferred← citeshttps://doi.org/10.4018/978-1-60566-404-0.ch005openalex-snapshot

Post-Processing for Rule Reduction Using Closed Set

inferred← citeshttps://doi.org/10.5555/1390681.1390700openalex-snapshot

Closed Sets for Labeled Data

inferred← citeshttps://doi.org/10.70675/b54520eeze245z411azbc6bzcc89958b0482openalex-snapshot

Un cadre générique pour les modèles globaux fondés sur les motifs locaux

Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.