On the public citation graph

Outlier Detection Based on Rough Membership Function

0 signed · 23 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/10.1007/11538059_42openalex-snapshot
An Optimization Model for Outlier Detection in Categorical Data
inferredcites →https://doi.org/10.1007/11548706_9openalex-snapshot
Outlier Detection Using Rough Set Theory
inferredcites →https://doi.org/10.1007/3-540-39205-x_46openalex-snapshot
Variable Precision Bayesian Rough Set Model
inferredcites →https://doi.org/10.1007/3-540-46145-0_17openalex-snapshot
Outlier Detection Using Replicator Neural Networks
inferredcites →https://doi.org/10.1007/978-94-011-3534-4openalex-snapshot
Rough Sets
inferredcites →https://doi.org/10.1007/978-94-015-3994-4openalex-snapshot
Identification of Outliers
inferredcites →https://doi.org/10.1007/bf01001956openalex-snapshot
Rough sets
inferredcites →https://doi.org/10.1016/j.eswa.2008.06.019openalex-snapshot
Some issues about outlier detection in rough set theory
inferredcites →https://doi.org/10.1016/j.ijar.2004.11.004openalex-snapshot
The investigation of the Bayesian rough set model
inferredcites →https://doi.org/10.1109/icdm.2002.1184035openalex-snapshot
A comparative study of RNN for outlier detection in data mining
inferredcites →https://doi.org/10.1145/335191.335388openalex-snapshot
LOF
inferredcites →https://doi.org/10.1145/342009.335388openalex-snapshot
LOF
inferredcites →https://doi.org/10.1145/375663.375668openalex-snapshot
Outlier detection for high dimensional data

What points at this work

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

inferred← citeshttps://doi.org/10.1007/978-3-319-54966-8_5openalex-snapshot
Rough Sets in Machine Learning: A Review
inferred← citeshttps://doi.org/10.1007/978-3-642-23713-3_9openalex-snapshot
A Rough Set Approach to Spatio-temporal Outlier Detection
inferred← citeshttps://doi.org/10.1016/j.dss.2015.05.002openalex-snapshot
Algorithm for the detection of outliers based on the theory of rough sets
inferred← citeshttps://doi.org/10.1016/j.eswa.2010.02.087openalex-snapshot
An information entropy-based approach to outlier detection in rough sets
inferred← citeshttps://doi.org/10.1016/j.socl.2021.100027openalex-snapshot
A fuzzy proximity relation approach for outlier detection in the mixed dataset by using rough entropy-based weighted density method
inferred← citeshttps://doi.org/10.1080/03081070701251182openalex-snapshot
A rough set approach to outlier detection
inferred← citeshttps://doi.org/10.1109/tkde.2012.234openalex-snapshot
Rough Sets, Kernel Set, and Spatiotemporal Outlier Detection
inferred← citeshttps://doi.org/10.1515/comp-2020-0228openalex-snapshot
Rough set-based entropy measure with weighted density outlier detection method
inferred← citeshttps://doi.org/10.19026/rjaset.8.986openalex-snapshot
Rough K-means Outlier Factor Based on Entropy Computation
inferred← citeshttps://doi.org/10.4018/ijfsa.2020070105openalex-snapshot
An Intuitionistic Fuzzy Approach With Rough Entropy Measure to Detect Outliers in Two Universal Sets
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.