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A critical review of rule surprisingness measures
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/3-540-45571-x_11openalex-snapshot
Exception Rule Mining with a Relative Interestingness Measure
inferredcites →https://doi.org/10.1007/bfb0094799openalex-snapshot
On objective measures of rule surprisingness
inferredcites →https://doi.org/10.1007/bfb0094800openalex-snapshot
Discovery of surprising exception rules based on intensity of implication
inferredcites →https://doi.org/10.1016/s0950-7051(99)00019-2openalex-snapshot
On rule interestingness measures
inferredcites →https://doi.org/10.1109/69.553165openalex-snapshot
What makes patterns interesting in knowledge discovery systems
inferredcites →https://doi.org/10.1109/cec.1999.782601openalex-snapshot
Discovering interesting prediction rules with a genetic algorithm
inferredcites →https://doi.org/10.1145/360402.360423openalex-snapshot
Understanding the crucial differences between classification and discovery of association rules
inferredcites →https://doi.org/10.1145/775047.775053openalex-snapshot
Selecting the right interestingness measure for association patterns
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/11564126_45openalex-snapshot
Evaluating the Correlation Between Objective Rule Interestingness Measures and Real Human Interest
inferred← citeshttps://doi.org/10.1007/978-3-540-71231-2_18openalex-snapshot
Detection of Sentinel Predictor-Class Associations with XCS: A Sensitivity Analysis
inferred← citeshttps://doi.org/10.1007/978-81-322-2208-8_8openalex-snapshot
A Study of Interestingness Measures for Knowledge Discovery in Databases—A Genetic Approach
inferred← citeshttps://doi.org/10.1016/j.asoc.2007.07.007openalex-snapshot
AISIID: An artificial immune system for interesting information discovery on the web
inferred← citeshttps://doi.org/10.1109/acct.2012.97openalex-snapshot
Revisiting Interestingness Measures for Knowledge Discovery in Databases
inferred← citeshttps://doi.org/10.1109/ical.2012.6308193openalex-snapshot
Associative classification using an immune optimization algorithm
inferred← citeshttps://doi.org/10.1109/rivf.2006.1696425openalex-snapshot
Extracting representative measures for the post-processing of association rules
inferred← citeshttps://doi.org/10.1109/tevc.2008.923394openalex-snapshot
Associative Classification With Artificial Immune System
inferred← citeshttps://doi.org/10.1145/1102256.1102269openalex-snapshot
Detection of sentinel predictor-class associations with XCS
inferred← citeshttps://doi.org/10.11606/d.55.2006.tde-05052006-111530openalex-snapshot
Identificação de regras de associação interessantes por meio de análises com medidas objetivas e subjetivas
inferred← citeshttps://doi.org/10.1504/ijista.2013.056535openalex-snapshot
An evolutionary approach to discover intra- and inter-class exceptions in databases
inferred← citeshttps://doi.org/10.1504/ijmic.2011.043153openalex-snapshot
Associative classification with evolutionary autonomous agents
inferred← citeshttps://doi.org/10.1504/ijmic.2013.052815openalex-snapshot
Directed clonal selection algorithm for associative classification
inferred← citeshttps://doi.org/10.18682/cyt.v1i1.791openalex-snapshot
Algoritmos Genéticos para la Extracción de Reglas de Predicción Interesantes Aplicadas al Posicionamiento de los Distintos Motores de Búsquedas
inferred← citeshttps://doi.org/10.4018/jdwm.2006010102openalex-snapshot
Discovering Surprising Instances of Simpson's Paradox in Hierarchical Multidimensional Data
inferred← citeshttps://doi.org/10.4114/ia.v10i32.924openalex-snapshot
A methodology for identifying interesting association rules by combining objective and subjective measures.
inferred← citeshttps://doi.org/10.5772/5522openalex-snapshot
MO-Miner: A Data Mining Tool Based on Multi-Objective Genetic Algorithms
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