Difference between revisions of "Hierarchical Cluster Analysis (HCA)"
(Created page with "[http://www.youtube.com/results?search_query=Hierarchical+Cluster+Analysis+%28HCA%29 YouTube search...] <youtube>bMH-aHNlhBA</youtube> <youtube>rg2cjfMsCk4</youtube> <youtube...") |
m |
||
| (13 intermediate revisions by the same user not shown) | |||
| Line 1: | Line 1: | ||
| − | + | {{#seo: | |
| + | |title=PRIMO.ai | ||
| + | |titlemode=append | ||
| + | |keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
| + | <!-- Google tag (gtag.js) --> | ||
| + | <script async src="https://www.googletagmanager.com/gtag/js?id=G-4GCWLBVJ7T"></script> | ||
| + | <script> | ||
| + | window.dataLayer = window.dataLayer || []; | ||
| + | function gtag(){dataLayer.push(arguments);} | ||
| + | gtag('js', new Date()); | ||
| + | |||
| + | gtag('config', 'G-4GCWLBVJ7T'); | ||
| + | </script> | ||
| + | }} | ||
| + | [https://www.youtube.com/results?search_query=Hierarchical+Cluster+Analysis+HCA YouTube search...] | ||
| + | [https://www.google.com/search?q=Hierarchical+Cluster+Analysis+HCA+deep+machine+learning+ML ...Google search] | ||
| + | |||
| + | * [[Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)]] | ||
| + | * [[Hierarchical Temporal Memory (HTM)]] | ||
| + | * [[Embedding]] ... [[Fine-tuning]] ... [[Retrieval-Augmented Generation (RAG)|RAG]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]]. [[...find outliers]] | ||
| + | |||
| + | # Identify clusters (items) with closest distance | ||
| + | # Join them to new clusters | ||
| + | # Compute distance between clusters (items) | ||
| + | # Return to step 1 | ||
| + | |||
| + | The HCPC (Hierarchical Clustering on Principal Components) approach allows us to combine the three standard methods used in multivariate data analyses (Husson, Josse, and J. 2010): | ||
| + | |||
| + | https://www.sthda.com/english/sthda-upload/figures/principal-component-methods/011-hcpc-hierarchical-clustering-on-principal-components-3d-map-1.png | ||
| + | |||
| + | <youtube>EQZaSuK-PHs</youtube> | ||
| + | <youtube>JcfIeaGzF8A</youtube> | ||
| + | <youtube>7BPLNOMNIXM</youtube> | ||
| + | <youtube>EUQY3hL38cw</youtube> | ||
<youtube>bMH-aHNlhBA</youtube> | <youtube>bMH-aHNlhBA</youtube> | ||
<youtube>rg2cjfMsCk4</youtube> | <youtube>rg2cjfMsCk4</youtube> | ||
Latest revision as of 08:58, 13 September 2023
YouTube search... ...Google search
- Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)
- Hierarchical Temporal Memory (HTM)
- Embedding ... Fine-tuning ... RAG ... Search ... Clustering ... Recommendation ... Anomaly Detection ... Classification ... Dimensional Reduction. ...find outliers
- Identify clusters (items) with closest distance
- Join them to new clusters
- Compute distance between clusters (items)
- Return to step 1
The HCPC (Hierarchical Clustering on Principal Components) approach allows us to combine the three standard methods used in multivariate data analyses (Husson, Josse, and J. 2010):