Clustering - Revision history

BPeat at 17:13, 16 September 2023

2023-09-16T17:13:24Z

BPeat at 09:35, 13 September 2023

2023-09-13T09:35:32Z

BPeat at 11:39, 17 August 2023

2023-08-17T11:39:32Z

BPeat at 00:10, 17 August 2023

2023-08-17T00:10:39Z

BPeat at 11:21, 16 August 2023

2023-08-16T11:21:03Z

BPeat at 18:12, 30 June 2023

2023-06-30T18:12:15Z

BPeat at 18:10, 27 June 2023

2023-06-27T18:10:23Z

BPeat: /* Unsupervised Learning */

2023-05-23T02:41:14Z

‎Unsupervised Learning

BPeat at 00:55, 23 May 2023

2023-05-23T00:55:15Z

BPeat at 00:52, 23 May 2023

2023-05-23T00:52:14Z

← Older revision		Revision as of 17:13, 16 September 2023
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	== News Headlines With Text Clustering ==		== News Headlines With Text Clustering ==
−	One way to use unsupervised learning for text clustering of news headlines is by using a model that employs unsupervised learning to automatically extract latent information from news articles with pre-determined topics. This model can use techniques such as Doc2vec to generate word vectors for each article. Afterward, a clustering algorithm such as spectral clustering can be applied to group the data based on similarity. This approach alleviates the need for humans to label news items manually. Another approach is to fine-tune pre-trained models unsupervised for text clustering, which simultaneously learns text representations and cluster assignments using a clustering oriented loss.	+	One way to use unsupervised learning for text clustering of news headlines is by using a model that employs unsupervised learning to automatically extract [[latent]] information from news articles with pre-determined topics. This model can use techniques such as Doc2vec to generate word vectors for each article. Afterward, a clustering algorithm such as spectral clustering can be applied to group the data based on similarity. This approach alleviates the need for humans to label news items manually. Another approach is to fine-tune pre-trained models unsupervised for text clustering, which simultaneously learns text representations and cluster assignments using a clustering oriented loss.

@@ Line 20: / Line 20: @@
 [https://www.bing.com/news/search?q=~Clustering+AI&qft=interval%3d%228%22 ...Bing News]
-* [[Embedding]] ... [[Fine-tuning]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
+* [[Embedding]] ... [[Fine-tuning]] ... [[Retrieval-Augmented Generation (RAG)|RAG]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
 ** [[Singular Value Decomposition (SVD)]]
 ** [[Principal Component Analysis (PCA)]]

@@ Line 39: / Line 39: @@
 *** [[Hierarchical Clustering;  Agglomerative (HAC) & Divisive (HDC)]]
 *** [[Hierarchical Temporal Memory (HTM)]]
-* [[Excel]] ... [[LangChain#Documents|Documents]] ... [[Database]] ... [[Graph]] ... [[LlamaIndex]]
+* [[Excel]] ... [[LangChain#Documents|Documents]] ... [[Database|Database; Vector & Relational]] ... [[Graph]] ... [[LlamaIndex]]
 = Similarity Measures for Clusters =

@@ Line 20: / Line 20: @@
 [https://www.bing.com/news/search?q=~Clustering+AI&qft=interval%3d%228%22 ...Bing News]
-* [[Embedding]] ... [[Fine-tuning]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]] ... [[...find outliers]]
+* [[Embedding]] ... [[Fine-tuning]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]].  [[...find outliers]]
 ** [[Singular Value Decomposition (SVD)]]
 ** [[Principal Component Analysis (PCA)]]

@@ Line 20: / Line 20: @@
 [https://www.bing.com/news/search?q=~Clustering+AI&qft=interval%3d%228%22 ...Bing News]
-* [[Embedding]]:  [[Agents#AI-Powered Search|Search]]  ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]] ... [[...find outliers]]
+* [[Embedding]] ... [[Fine-tuning]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]] ... [[...find outliers]]
 ** [[Singular Value Decomposition (SVD)]]
 ** [[Principal Component Analysis (PCA)]]

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|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
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [https://www.youtube.com/results?search_query=~Clustering+AI YouTube]

← Older revision		Revision as of 02:41, 23 May 2023
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	= Unsupervised Learning =		= Unsupervised Learning =
		+	The main types of clustering in unsupervised machine learning include K-means, hierarchical clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Gaussian Mixtures Model (GMM).
		+
		+	== News Headlines With Text Clustering ==
		+	One way to use unsupervised learning for text clustering of news headlines is by using a model that employs unsupervised learning to automatically extract latent information from news articles with pre-determined topics. This model can use techniques such as Doc2vec to generate word vectors for each article. Afterward, a clustering algorithm such as spectral clustering can be applied to group the data based on similarity. This approach alleviates the need for humans to label news items manually. Another approach is to fine-tune pre-trained models unsupervised for text clustering, which simultaneously learns text representations and cluster assignments using a clustering oriented loss.

−	~~== Feature Extraction ==~~
−	~~<youtube>Muf8EonV7Q0</youtube>~~

−	~~== News Headlines With Text Clustering ==~~
	<youtube>23qfPq0m7XA</youtube>		<youtube>23qfPq0m7XA</youtube>
		+
		+	= Feature Extraction =
		+	Feature extraction is an efficient approach for alleviating the issue of dimensionality in high-dimensional data. Unsupervised feature extraction projects high-dimensional data into a low-dimensional subspace while preserving similarity. It generates low-dimensional features without considering any explicit semantic labels. This can be done using unsupervised learning methods such as transformations (e.g., PCA/ICA/NMF), embeddings (e.g., T-distributed stochastic neighbor embedding), cluster-based methods (e.g., k-means), and kernel-based methods (e.g., kernel PCA).
		+
		+
		+	<youtube>Muf8EonV7Q0</youtube>

@@ Line 25: / Line 25: @@
 ** [[Variational Autoencoder (VAE)]]
 ** [[Biclustering]]
 ** [https://en.wikipedia.org/wiki/Multidimensional_scaling Multidimensional Scaling (MDS)]
 ** Hierarchical; to include clustering
@@ Line 40: / Line 41: @@
 [https://girke.bioinformatics.ucr.edu/GEN242/mydoc_Rclustering_3.html#example-2 Clustering Algorithms | Data Analysis in Genome Biology]
 = Unsupervised Learning =

@@ Line 40: / Line 40: @@
 [https://girke.bioinformatics.ucr.edu/GEN242/mydoc_Rclustering_3.html#example-2 Clustering Algorithms | Data Analysis in Genome Biology]
-= OPTICS: ordering points to identify the clustering structure =
+= [[OPTICS: Ordering Points To Identify the Clustering Structure]] =
 * [https://www.dbs.ifi.lmu.de/Publikationen/Papers/OPTICS.pdf OPTICS: ordering points to identify the clustering structure (PDF)| M. Ankerst, M. Breunig, H. Kriegel, J. Sander - Institute for Computer Science, University of Munich]