Evidence map›Paper›PMID 38785624›Full record

ArticleEntropy (Basel, Switzerland)2024

Fast Fusion Clustering via Double Random Projection.

Hongni Wang, Na Li, Yanqiu Zhou, Jingxin Yan, Bei Jiang, Linglong Kong, Xiaodong Yan

Abstract read
In one paragraph

Article in Entropy (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Hongni WangSchool of Statistics and Mathematics, Shandong University of Finance and Economics, Jinan 250014, China.
Na LiSchool of Statistics and Mathematics, Shandong University of Finance and Economics, Jinan 250014, China.
Yanqiu ZhouSchool of Science, Guangxi University of Science and Technology, Liuzhou 545006, China.
Jingxin YanAcademy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
Bei JiangDepartment of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2G1, Canada.
Linglong KongDepartment of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB T6G 2G1, Canada.ORCID 0000-0003-3011-9216
Xiaodong YanZhongtai Securities Institute for Financial Studies, Shandong University, Jinan 250100, China.ORCID 0000-0001-9972-6888

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In unsupervised learning, clustering is a common starting point for data processing. The convex or concave fusion clustering method is a novel approach that is more stable and accurate than traditional methods such as

Indexed as

ADMM algorithmfusion clusteringrandom projectionunsupervised learning

Identifiers

PMID38785624
PMCPMC11119451

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.