REPOGEO REPORT · LITE

zhoushengisnoob/DeepClustering

Default branch master · commit a006b1e8 · scanned 6/22/2026, 6:03:00 PM

GitHub: 3,057 stars · 423 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

30 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 0 warn · 1 fail

Objective metadata checks

AI knows your name

3 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface zhoushengisnoob/DeepClustering, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

hightopics#1

Add relevant topics to the repository

Why:

COPY-PASTE FIX

deep-clustering, machine-learning, survey, literature-review, research, computer-vision, unsupervised-learning, multi-view-clustering, graph-clustering

highlicense#2

Add a LICENSE file to the repository

Why:

COPY-PASTE FIX

Create a LICENSE file in the repository root. For example, using the MIT License template is a common choice for open-source projects.

mediumhomepage#3

Set the repository homepage URL

Why:

COPY-PASTE FIX

Set the repository homepage URL to the official publication link of your survey paper, 'A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions', once available.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface zhoushengisnoob/DeepClustering

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

tkipf/gae

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

tkipf/gae · recommended 2×
zhumeihua/ARGA · recommended 2×
Deep Clustering: A Review by Aljalbout et al. (2018/2020) · recommended 1×
A Survey on Deep Clustering Methods by Min et al. (2018) · recommended 1×
Deep Learning for Clustering: A Review by Xu and Tian (2015) · recommended 1×

CATEGORY QUERY
Where can I find a comprehensive survey of deep learning techniques for data clustering?
you: not recommended
AI recommended (in order):
1. Deep Clustering: A Review by Aljalbout et al. (2018/2020)
2. A Survey on Deep Clustering Methods by Min et al. (2018)
3. Deep Learning for Clustering: A Review by Xu and Tian (2015)
4. Deep Clustering: A Comprehensive Review by Guo et al. (2021)
5. Deep Clustering: An Overview by Fard et al. (2020)
6. Deep Clustering: A Survey of Recent Advances by Li et al. (2021)
7. Deep Clustering: A Review of Methods and Applications by Wang et al. (2022)
8. arXiv
9. Google Scholar
10. ResearchGate
AI recommended 10 alternatives but never named zhoushengisnoob/DeepClustering. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What are the key papers and codebases for multi-view or graph deep clustering?
you: not recommended
AI recommended (in order):
1. DIMC-GCN (linzihang/DIMC-GCN)
2. DMVC (csuhan/DMVC)
3. AMVC (wangxiao5791509/AMVC)
4. DGI (PetarV-/DGI)
5. PyTorch Geometric (pyg-team/pytorch_geometric)
6. Deep Graph Library (dmlc/dgl)
7. GAE (tkipf/gae)
8. VGAE (tkipf/gae)
9. ARGA (zhumeihua/ARGA)
10. ARGVA (zhumeihua/ARGA)
AI recommended 10 alternatives but never named zhoushengisnoob/DeepClustering. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
fail
Suggestion:
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of zhoushengisnoob/DeepClustering?
pass
AI named zhoushengisnoob/DeepClustering explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts zhoushengisnoob/DeepClustering in production, what risks or prerequisites should they evaluate first?
pass
AI named zhoushengisnoob/DeepClustering explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo zhoushengisnoob/DeepClustering solve, and who is the primary audience?
pass
AI named zhoushengisnoob/DeepClustering explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of zhoushengisnoob/DeepClustering. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/zhoushengisnoob/DeepClustering.svg)](https://repogeo.com/en/r/zhoushengisnoob/DeepClustering)

HTML

<a href="https://repogeo.com/en/r/zhoushengisnoob/DeepClustering"><img src="https://repogeo.com/badge/zhoushengisnoob/DeepClustering.svg" alt="RepoGEO" /></a>

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zhoushengisnoob/DeepClustering — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite