On the public citation graph

An Efficient Framework for Mining Association Rules in the Distributed Databases

0 signed · 14 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 See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.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/10.1016/j.ins.2010.07.020openalex-snapshot

Toward boosting distributed association rule mining by data de-clustering

inferredcites →https://doi.org/10.1109/69.553164openalex-snapshot

Parallel mining of association rules

inferredcites →https://doi.org/10.1109/69.846291openalex-snapshot

Scalable algorithms for association mining

inferredcites →https://doi.org/10.1109/tkde.2013.41openalex-snapshot

Secure Mining of Association Rules in Horizontally Distributed Databases

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.4519456openalex-extracted

Exploring the viability of Smart Public Universities in the Philippines’ Zamboanga Peninsula Region

inferred← citeshttps://doi.org/10.1016/j.eswa.2017.09.027openalex-snapshot

Segmentation of dental X-ray images in medical imaging using neutrosophic orthogonal matrices

inferred← citeshttps://doi.org/10.1016/j.knosys.2018.04.038openalex-snapshot

ARM–AMO: An efficient association rule mining algorithm based on animal migration optimization

inferred← citeshttps://doi.org/10.1049/iet-ipr.2018.5555openalex-snapshot

Highly efficient neoteric histogram–entropy‐based rapid and automatic thresholding method for moving vehicles and pedestrians detection

inferred← citeshttps://doi.org/10.1093/comjnl/bxab164openalex-snapshot

Rare Correlated Coherent Association Rule Mining With CLS-MMS

inferred← citeshttps://doi.org/10.1109/access.2020.2995719openalex-snapshot

A Fast Approach for Up-Scaling Frequent Itemsets

inferred← citeshttps://doi.org/10.1109/ictus.2017.8286070openalex-snapshot

Efficient random data accessing in MapReduce

inferred← citeshttps://doi.org/10.14293/pr2199.000227.v1openalex-snapshot

Exploring the viability of Smart Public Universities in Zamboanga Peninsula (Region IX). Philippines

inferred← citeshttps://doi.org/10.17762/turcomat.v12i5.1732openalex-snapshot

An Efficient Method for Mining Distributed Frequent Itemsets: MDFI

inferred← citeshttps://doi.org/10.4018/ijismd.2019010103openalex-snapshot

Adaptive Threshold Based Clustering

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.