Fuzzy C-Means (FCM) - Revision history

BPeat at 03:01, 6 March 2024

2024-03-06T03:01:10Z

BPeat at 15:45, 13 September 2023

2023-09-13T15:45:16Z

BPeat at 00:14, 17 August 2023

2023-08-17T00:14:45Z

BPeat at 00:39, 13 July 2023

2023-07-13T00:39:45Z

BPeat at 01:22, 8 July 2023

2023-07-08T01:22:53Z

BPeat: Text replacement - "http:" to "https:"

2023-03-28T18:32:08Z

Text replacement - "http:" to "https:"

BPeat at 00:37, 23 April 2019

2019-04-23T00:37:58Z

BPeat at 00:04, 23 April 2019

2019-04-23T00:04:13Z

BPeat at 23:27, 22 April 2019

2019-04-22T23:27:20Z

BPeat at 23:24, 22 April 2019

2019-04-22T23:24:34Z

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=Fuzzy+C+Means+clustering ...Google search]
-* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]]
 ** [[...cluster]]
 * [[K-Means]]

@@ Line 20: / Line 20: @@
 ** [[...cluster]]
 * [[K-Means]]
-* [[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]]
 * [https://www.sciencedirect.com/science/article/pii/0098300484900207 FCM: The fuzzy c-means clustering algorithm | J. Bezdek, R. Ehrlich, and W. Full - ScienceDirect]

@@ Line 20: / Line 20: @@
 ** [[...cluster]]
 * [[K-Means]]
-* [[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]]
 * [https://www.sciencedirect.com/science/article/pii/0098300484900207 FCM: The fuzzy c-means clustering algorithm | J. Bezdek, R. Ehrlich, and W. Full - ScienceDirect]

@@ Line 17: / Line 17: @@
 [https://www.google.com/search?q=Fuzzy+C+Means+clustering ...Google search]
-* [[AI Solver]]
+* [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Optimizer]] ... [[Train, Validate, and Test]]
 ** [[...cluster]]
 * [[K-Means]]

@@ 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=Fuzzy+C+Means+clustering YouTube search...]
@@ Line 10: / Line 19: @@
 * [[AI Solver]]
 ** [[...cluster]]
 * [[K-Means]]
-* [[Clustering]]
+* [[Embedding]]:  [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]] ... [[...find outliers]]
 * [https://www.sciencedirect.com/science/article/pii/0098300484900207 FCM: The fuzzy c-means clustering algorithm | J. Bezdek, R. Ehrlich, and W. Full - ScienceDirect]

@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=Fuzzy+C+Means+clustering YouTube search...]
+[https://www.youtube.com/results?search_query=Fuzzy+C+Means+clustering YouTube search...]
-[http://www.google.com/search?q=Fuzzy+C+Means+clustering ...Google search]
+[https://www.google.com/search?q=Fuzzy+C+Means+clustering ...Google search]
 * [[AI Solver]]
@@ Line 13: / Line 13: @@
 * [[K-Means]]
 * [[Clustering]]
-* [http://www.sciencedirect.com/science/article/pii/0098300484900207 FCM: The fuzzy c-means clustering algorithm | J. Bezdek, R. Ehrlich, and W. Full - ScienceDirect]
+* [https://www.sciencedirect.com/science/article/pii/0098300484900207 FCM: The fuzzy c-means clustering algorithm | J. Bezdek, R. Ehrlich, and W. Full - ScienceDirect]
 One of the most widely used fuzzy clustering algorithms is the Fuzzy C-means clustering (FCM) Algorithm. Fuzzy logic principles can be used to cluster multidimensional data, assigning each point a membership in each cluster center from 0 to 100 percent. This can be very powerful compared to traditional hard-thresholded clustering where every point is assigned a crisp, exact label.
@@ Line 25: / Line 25: @@
 ## For each data point, compute its coefficients of being in the clusters.
-http://pythonhosted.org/scikit-fuzzy/_images/plot_cmeans_1.png
+https://pythonhosted.org/scikit-fuzzy/_images/plot_cmeans_1.png
-[http://pythonhosted.org/scikit-fuzzy/auto_examples/plot_cmeans.html Fuzzy c-means clustering]
+[https://pythonhosted.org/scikit-fuzzy/auto_examples/plot_cmeans.html Fuzzy c-means clustering]
 <youtube>SdyukrDWTe0</youtube>

@@ Line 21: / Line 21: @@
 # Assign coefficients randomly to each data point for being in the clusters.
 # Repeat until the algorithm has converged (that is, the coefficients' change between two iterations is no more than E, the given sensitivity threshold) :
-* Compute the centroid for each cluster.
+## Compute the centroid for each cluster.
-* For each data point, compute its coefficients of being in the clusters.
+## For each data point, compute its coefficients of being in the clusters.
 http://pythonhosted.org/scikit-fuzzy/_images/plot_cmeans_1.png

@@ Line 12: / Line 12: @@
 * [[Capabilities]]
 * [[K-Means]]
-<youtube>_aWzGGNrcic</youtube>
+One of the most widely used fuzzy clustering algorithms is the Fuzzy C-means clustering (FCM) Algorithm. Fuzzy logic principles can be used to cluster multidimensional data, assigning each point a membership in each cluster center from 0 to 100 percent. This can be very powerful compared to traditional hard-thresholded clustering where every point is assigned a crisp, exact label.
-<youtube>xtDMHPVDDK</youtube>
-<youtube>_aWzGGNrcic</youtube>
+The fuzzy c-means algorithm is very similar to the k-means algorithm:
-<youtube>_aWzGGNrcic</youtube>