Paper Citation Record · LEDGER

Fast Clustering of Categorical Big Data

As of 22 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2502.07081.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.07081 v2

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:54:36.483348Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

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Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

42 of 42 outbound references displayed

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External citation measurements

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Outbound references

Observation 5fcc1d63-a273-47d2-9083-62e59027756e · outbound

This paper cites Some methods for classification and analysis of multivariate observations.

Fast Clustering of Categorical Big Data Some methods for classification and analysis of multivariate observations

Reference 1

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This paper cites A fast clustering algorithm to cluster very large categorical data sets in data mining.

Fast Clustering of Categorical Big Data A fast clustering algorithm to cluster very large categorical data sets in data mining

Reference 2

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Observation 536c3873-d200-461e-974b-82be74aa4c07 · outbound

This paper cites Extensions to the k-means algorithm for clustering large data sets with categorical values.

Fast Clustering of Categorical Big Data Extensions to the k-means algorithm for clustering large data sets with categorical values

Reference 3

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This paper cites A new initialization method for categorical data clustering.

Fast Clustering of Categorical Big Data A new initialization method for categorical data clustering

Reference 4

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This paper cites Cluster center initialization algorithm for k-modes clustering.

Fast Clustering of Categorical Big Data Cluster center initialization algorithm for k-modes clustering

Reference 5

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This paper cites Fast global k-means clustering based on local geometrical information.

Fast Clustering of Categorical Big Data Fast global k-means clustering based on local geometrical information

Reference 6

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Observation e6ddd945-dabc-4988-a332-d0f97b394400 · outbound

This paper cites An iterative initial-points refinement algorithm for categorical data clustering.

Fast Clustering of Categorical Big Data An iterative initial-points refinement algorithm for categorical data clustering

Reference 7

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Observation 82f52652-c747-4a9b-a2dc-c9c800c0de41 · outbound

This paper cites Refining initial points for k-means clustering.

Fast Clustering of Categorical Big Data Refining initial points for k-means clustering

Reference 8

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This paper cites A new initialization method for clustering categorical data.

Fast Clustering of Categorical Big Data A new initialization method for clustering categorical data

Reference 9

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This paper cites An initialization method to simultaneously find initial cluster centers and the number of clusters for clustering categorical data.

Fast Clustering of Categorical Big Data An initialization method to simultaneously find initial cluster centers and the number of clusters for clustering categorical data

Reference 10

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Observation 96e25cb4-6659-4b8a-8750-cbe6defc29a5 · outbound

This paper cites A global k-modes algorithm for clustering categorical data.

Fast Clustering of Categorical Big Data A global k-modes algorithm for clustering categorical data

Reference 11

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Observation 7941b500-ae15-42c5-9254-267d914ebc0f · outbound

This paper cites Initialization of k-modes clustering using outlier detection techniques.

Fast Clustering of Categorical Big Data Initialization of k-modes clustering using outlier detection techniques

Reference 12

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Observation 59a7b887-f653-40ca-87f9-c0beb693c85e · outbound

This paper cites UCI machine learning repository, 2017.

Fast Clustering of Categorical Big Data UCI machine learning repository, 2017

Reference 13

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This paper cites An evaluation of statistical approaches to text categorization.

Fast Clustering of Categorical Big Data An evaluation of statistical approaches to text categorization

Reference 14

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Observation 04ec8664-f441-48b4-adf3-c656ba6cdde4 · outbound

This paper cites Cluster analysis for applications accademic press.

Fast Clustering of Categorical Big Data Cluster analysis for applications accademic press

Reference 15

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Observation b206b656-85c9-46a9-93de-b36dca70408a · outbound

This paper cites A comparison of document clustering techniques.

Fast Clustering of Categorical Big Data A comparison of document clustering techniques

Reference 16

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This paper cites A limited-iteration bisecting k-means for fast clustering large datasets.

Fast Clustering of Categorical Big Data A limited-iteration bisecting k-means for fast clustering large datasets

Reference 17

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This paper cites Computation of initial modes for k-modes clustering algorithm using evidence accumulation.

Fast Clustering of Categorical Big Data Computation of initial modes for k-modes clustering algorithm using evidence accumulation

Reference 18

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This paper cites A cluster centers initialization method for clustering categorical data.

Fast Clustering of Categorical Big Data A cluster centers initialization method for clustering categorical data

Reference 19

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This paper cites A linear method for deviation detection in large databases.

Fast Clustering of Categorical Big Data A linear method for deviation detection in large databases

Reference 20

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Observation 66d06a82-7df8-4c99-9458-ce858ae1dad4 · outbound

This paper cites Lof: identifying density-based local outliers.

Fast Clustering of Categorical Big Data Lof: identifying density-based local outliers

Reference 21

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Fast Clustering of Categorical Big Data Unresolved cited work

Reference 22

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Observation 5d10f319-908f-40af-9a2e-5f1096329b68 · outbound

This paper cites Computing initial points using density based multiscale data condensation for clustering categorical data.

Fast Clustering of Categorical Big Data Computing initial points using density based multiscale data condensation for clustering categorical data

Reference 23

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This paper cites Data clustering using evidence accumulation.

Fast Clustering of Categorical Big Data Data clustering using evidence accumulation

Reference 24

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Observation dd976241-bc37-4510-9729-3653b98a2d7e · outbound

This paper cites Farthest-Point Heuristic based Initialization Methods for K-Modes Clustering.

Fast Clustering of Categorical Big Data Farthest-Point Heuristic based Initialization Methods for K-Modes Clustering

Reference 25

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This paper cites Clustering to minimize the maximum intercluster distance.

Fast Clustering of Categorical Big Data Clustering to minimize the maximum intercluster distance

Reference 26

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This paper cites A fast deep learning method for security vulnerability study of xor pufs.

Fast Clustering of Categorical Big Data A fast deep learning method for security vulnerability study of xor pufs

Reference 27

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Fast Clustering of Categorical Big Data A hybrid-optimizer-enhanced neural network method for the security vulnerability study of multiplexer arbiter pufs

Reference 28

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Fast Clustering of Categorical Big Data Extensive examination of xor arbiter pufs as security primitives for resource-constrained iot devices

Reference 29

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This paper cites A machine learning-based security vulnerability study on xor pufs for resource-constraint internet of things.

Fast Clustering of Categorical Big Data A machine learning-based security vulnerability study on xor pufs for resource-constraint internet of things

Reference 30

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Fast Clustering of Categorical Big Data A conceptual version of the k-means algorithm

Reference 31

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Fast Clustering of Categorical Big Data Symbolic clustering using a new dissimilarity measure

Reference 32

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This paper cites Rock: A robust clustering algorithm for categorical attributes.

Fast Clustering of Categorical Big Data Rock: A robust clustering algorithm for categorical attributes

Reference 33

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This paper cites ECGN: A Cluster-Aware Approach to Graph Neural Networks for Imbalanced Classification.

Fast Clustering of Categorical Big Data ECGN: A Cluster-Aware Approach to Graph Neural Networks for Imbalanced Classification

Reference 34

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This paper cites Calhoun, and Jingyu Liu.

Fast Clustering of Categorical Big Data Calhoun, and Jingyu Liu

Reference 35

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Fast Clustering of Categorical Big Data Graph-based deep learning models in the prediction of early-stage alzheimers

Reference 36

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Fast Clustering of Categorical Big Data Calhoun, and Jingyu Liu

Reference 37

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Observation a4bad1d9-4a3d-46e9-8f92-36f1488163c5 · outbound

This paper cites Unsupervised Deep Embedding for Clustering Analysis.

Fast Clustering of Categorical Big Data Unsupervised Deep Embedding for Clustering Analysis

Reference 38

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Observation b1e9e75e-b598-44f7-b703-7f82dffd6c88 · outbound

This paper cites Clustering with Deep Learning: Taxonomy and New Methods.

Fast Clustering of Categorical Big Data Clustering with Deep Learning: Taxonomy and New Methods

Reference 39

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Fast Clustering of Categorical Big Data Unresolved cited work

Reference 40

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Observation 9f51d202-3873-45be-8569-bf6e9f2382c2 · outbound

This paper cites Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity.

Fast Clustering of Categorical Big Data Self-Clustering Graph Transformer Approach to Model Resting-State Functional Brain Activity

Reference 41

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Observation 5bd82d1d-4e4f-49ac-8f4a-d6dbb53551d2 · outbound

This paper cites Deep learning for community detection: Progress, challenges and opportunities.

Fast Clustering of Categorical Big Data Deep learning for community detection: Progress, challenges and opportunities

Reference 42

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

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