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On characterization and discovery of minimal unexpected patterns in rule discovery

https://doi.org/10.1109/tkde.2006.32

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What this work says

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

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

Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets

inferredcites →https://doi.org/10.1007/3-540-45372-5_8openalex-snapshot

Approximation of Frequency Queries by Means of Free-Sets

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

Mining All Non-derivable Frequent Itemsets

inferredcites →https://doi.org/10.1007/3-540-47887-6_13openalex-snapshot

Pruning Redundant Association Rules Using Maximum Entropy Principle

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

Discovering Frequent Closed Itemsets for Association Rules

inferredcites →https://doi.org/10.1007/3-540-64383-4_7openalex-snapshot

Interestingness of discovered association rules in terms of neighborhood-based unexpectedness

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/10.1007/bfb0053707openalex-snapshot

Discovering unexpected patterns in temporal data using temporal logic

inferredcites →https://doi.org/10.1016/0004-3702(82)90040-6openalex-snapshot

Generalization as search

inferredcites →https://doi.org/10.1016/b978-0-12-737550-2.50018-xopenalex-snapshot

INDUCTIVE LEARNING OF RELATIONAL PRODUCTIONS

inferredcites →https://doi.org/10.1016/b978-0-12-737550-2.50019-1openalex-snapshot

MODEL-DIRECTED LEARNING OF PRODUCTION RULES11This work was supported by the Advanced Research Projects Agency under contract DAHC 15-73-C-0435, and by the National Institutes of Health under grant RR 00612–07.

inferredcites →https://doi.org/10.1016/s0167-9236(99)00053-6openalex-snapshot

Unexpectedness as a measure of interestingness in knowledge discovery

inferredcites →https://doi.org/10.1093/acprof:oso/9780195158557.003.0003openalex-snapshot

What Is Interesting?

inferredcites →https://doi.org/10.1109/69.553165openalex-snapshot

What makes patterns interesting in knowledge discovery systems

inferredcites →https://doi.org/10.1109/69.940730openalex-snapshot

A new approach to online generation of association rules

inferredcites →https://doi.org/10.1109/icde.1999.754924openalex-snapshot

Constraint-based rule mining in large, dense databases

inferredcites →https://doi.org/10.1109/icdm.2001.989556openalex-snapshot

Interestingness preprocessing

inferredcites →https://doi.org/10.1109/tkde.2000.868901openalex-snapshot

Building probabilistic networks: "Where do the numbers come from?" guest editors' introduction

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/10.1145/1014052.1014094openalex-snapshot

On the discovery of significant statistical quantitative rules

inferredcites →https://doi.org/10.1145/1045343.1045370openalex-snapshot

Inductive learning of relational productions

inferredcites →https://doi.org/10.1145/1045343.1045371openalex-snapshot

Model-directed learning of production rules

inferredcites →https://doi.org/10.1145/170035.170072openalex-snapshot

Mining association rules between sets of items in large databases

inferredcites →https://doi.org/10.1145/170036.170072openalex-snapshot

Mining association rules between sets of items in large databases

inferredcites →https://doi.org/10.1145/233269.233311openalex-snapshot

Mining quantitative association rules in large relational tables

inferredcites →https://doi.org/10.1145/235968.233311openalex-snapshot

Mining quantitative association rules in large relational tables

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

Dynamic itemset counting and implication rules for market basket data

inferredcites →https://doi.org/10.1145/253262.253325openalex-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/10.1145/276305.276313openalex-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/10.1145/347090.347101openalex-snapshot

Generating non-redundant association rules

inferredcites →https://doi.org/10.1145/347090.347103openalex-snapshot

Small is beautiful

inferredcites →https://doi.org/10.1145/956750.956780openalex-snapshot

Mining unexpected rules by pushing user dynamics

inferredcites →https://doi.org/10.1145/956755.956780openalex-snapshot

Mining unexpected rules by pushing user dynamics

inferredcites →https://doi.org/10.21236/ada042721openalex-snapshot

Model-Directed Learning of Production Rules

What points at this work

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

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Mining Unexpected Web Usage Behaviors

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Ontology-Based Fraud Detection

inferred← citeshttps://doi.org/10.1007/978-3-540-78921-5_2openalex-snapshot

First Elements on Knowledge Discovery Guided by Domain Knowledge (KDDK)

inferred← citeshttps://doi.org/10.1007/978-3-540-78983-3_21openalex-snapshot

User-System Interaction for Redundancy-Free Knowledge Discovery in Data

inferred← citeshttps://doi.org/10.1007/978-3-642-24443-8_18openalex-snapshot

Conceptual Distance for Association Rules Post-processing

inferred← citeshttps://doi.org/10.1007/978-3-642-25853-4_3openalex-snapshot

A False Negative Maximal Frequent Itemset Mining Algorithm over Stream

inferred← citeshttps://doi.org/10.1016/j.artmed.2007.07.005openalex-snapshot

Evaluation of rule interestingness measures in medical knowledge discovery in databases

inferred← citeshttps://doi.org/10.1016/j.datak.2009.01.002openalex-snapshot

Mining non-derivable frequent itemsets over data stream

inferred← citeshttps://doi.org/10.1016/j.dss.2008.09.003openalex-snapshot

A change detection method for sequential patterns

inferred← citeshttps://doi.org/10.1016/j.ins.2016.08.016openalex-snapshot

Towards automation of knowledge understanding: An approach for probabilistic generative classifiers

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

Unexpected rules using a conceptual distance based on fuzzy ontology

inferred← citeshttps://doi.org/10.1016/j.knosys.2009.05.009openalex-snapshot

Discovering unexpected documents in corpora

inferred← citeshttps://doi.org/10.1080/08839514.2014.875679openalex-snapshot

MINING SURPRISING PATTERNS AND THEIR EXPLANATIONS IN CLINICAL DATA

inferred← citeshttps://doi.org/10.1109/cis.2011.281openalex-snapshot

A Simple But Effective Stream Maximal Frequent Itemset Mining Algorithm

inferred← citeshttps://doi.org/10.1109/fskd.2010.5569206openalex-snapshot

Mining maximal frequent itemsets on graphics processors

inferred← citeshttps://doi.org/10.1109/tkde.2011.100openalex-snapshot

Discovery of Delta Closed Patterns and Noninduced Patterns from Sequences

inferred← citeshttps://doi.org/10.1142/s0218488509006054openalex-snapshot

DISCOVERING FUZZY UNEXPECTED SEQUENCES WITH CONCEPT HIERARCHIES

inferred← citeshttps://doi.org/10.1142/s0219622011004233openalex-snapshot

A TIME-INTERVAL SEQUENTIAL PATTERN CHANGE DETECTION METHOD

inferred← citeshttps://doi.org/10.1145/1150402.1150532openalex-snapshot

Camouflaged fraud detection in domains with complex relationships

inferred← citeshttps://doi.org/10.1145/1456223.1456235openalex-snapshot

Recognizing unexpected recurrence behaviors with fuzzy measures in sequence databases

inferred← citeshttps://doi.org/10.1145/2559952openalex-snapshot

On Unexpectedness in Recommender Systems

inferred← citeshttps://doi.org/10.1145/2700484openalex-snapshot

Significant Correlation Pattern Mining in Smart Homes

inferred← citeshttps://doi.org/10.1287/isre.2021.1033openalex-snapshot

Modifying Transactional Databases to Hide Sensitive Association Rules

inferred← citeshttps://doi.org/10.1504/ijbidm.2011.038276openalex-snapshot

WebUser: mining unexpected web usage

inferred← citeshttps://doi.org/10.3233/ida-150721openalex-snapshot

Efficient mining of new concise representations of rare correlated patterns1

inferred← citeshttps://doi.org/10.3233/ida-2010-0407openalex-snapshot

Extraction of unexpected sentences: A sentiment classification assessed approach

inferred← citeshttps://doi.org/10.4018/978-1-60566-010-3.ch175openalex-snapshot

Learning Exceptions to Refine a Domain Expertise

inferred← citeshttps://doi.org/10.4304/jsw.7.1.25-32openalex-snapshot

A Simple but Effective Maximal Frequent Itemset Mining Algorithm over Streams

inferred← citeshttps://doi.org/10.48550/arxiv.1810.07116openalex-snapshot

Motifs corrélés rares : Caractérisation et nouvelles représentations concises

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Generalised Interaction Mining: Probabilistic, Statistical and Vectorised Methods in High Dimensional or Uncertain Databases

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