Unsupervised learning

2022-05-09T14:31:56Z

Sebastian: /* Resources */

User:Sebastian

2022-05-09T05:07:33Z

Sebastian:

Vipul: "ok, so for ML wiki, I think you should pick some page or pages to write fully (i.e., long pages) and discuss with @Issa and me as you're doing it, so we can thnink through the right structure of the pages Vipul In parallel, you can continue the process of creating small, stub pages as you learn things Vipul Vipul Naik That's the T-shaped idea: do a few things deeply and then a lot of things (wide) and later you can deepen those other things."

All existing pages here[https://machinelearning.subwiki.org/wiki/Special:AllPages?fbclid=IwAR0TX3jIjbxD2WB-Vn8hqLkaQ3w2hDUcFyzW48ql9SaovFyzbR6M7cyq4yk]

Red links:

* [[Agglomerative clustering]] ([https://www.youtube.com/watch?v=IUn8k5zSI6g])
* [[Anomaly detection]]
* [[Artificial intelligence]]
* [[Bayesian linear regression]]
* [[Bit]]
* [[Byte]]
* [[Centroid linkage clustering]]
* [[Classification]]
* [[Complete linkage clustering]]
* [[Data mining]]
* [[Dendogram]]
* [[Evaluation metrics]]
* [[Expert system]]
* [[Hierarchical clustering]]
* [[Machine learning algorithms]]
* [[Machine learning applications]]
* [[Mean linkage clustering]]
* [[Multiple regression]]
* [[Ordinal regression]]
* [[Partitional clustering]]
* [[Poisson regression]]
* [[Reinforcement learning]]
* [[Semi-supervised learning]]
* [[Simple regression]] ([[Simple linear regression]], [[Simple non-linear regression]])
* [[Single linkage clustering]]
* [[Unsupervised learning]] ([[clustering]], [[dimensionality reduction]], [[recommender system]]s, [[deep learning]], [[Density estimation]], [[Market basket analysis]])

* [[Fast forest quantile regression]]
* [[Linear regression]] (expand)
* [[Polynomial regression]]
* [[Lasso regression]]
* [[Stepwise regression]]
* [[Ridge regression]]

* [[Neural network regression]]
* [[Decision forest regression]]
* [[Regression model]]
* [[Boosted decision tree regression]]
* [[Python]]
* [[NumPy]]
* [[scikit-learn]]
* [[Pytorch]]
* [[SciPy]]
* [[matplotlib]]
* [[pandas]]
* [[Dataset]]
* [[Standard deviation]]
* [[TensorFlow]]
* [[Training accuracy]]
* [[Out-of-sample accuracy]]
* [[Procgen Benchmark]]
* [[Optimization]]

* [[Silhouette score]] ([https://www.youtube.com/watch?v=IUn8k5zSI6g])
* [[Soft computing]]

* [[K-nearest neighbor]]

* [[NumPy]] [https://www.coursera.org/lecture/machine-learning-with-python/python-for-machine-learning-WQgHa]
* [[SciPy]] [https://www.coursera.org/lecture/machine-learning-with-python/python-for-machine-learning-WQgHa]
* [[matplotlib]] [https://www.coursera.org/lecture/machine-learning-with-python/python-for-machine-learning-WQgHa]
* [[pandas]] [https://www.coursera.org/lecture/machine-learning-with-python/python-for-machine-learning-WQgHa]
* [[scikit-learn]] [https://www.coursera.org/lecture/machine-learning-with-python/python-for-machine-learning-WQgHa]
* [[Clustering]] [https://www.coursera.org/lecture/machine-learning-with-python/supervised-vs-unsupervised-jgzpX]

User:Sebastian

2022-05-09T04:16:28Z

Sebastian:

Vipul: "ok, so for ML wiki, I think you should pick some page or pages to write fully (i.e., long pages) and discuss with @Issa and me as you're doing it, so we can thnink through the right structure of the pages Vipul In parallel, you can continue the process of creating small, stub pages as you learn things Vipul Vipul Naik That's the T-shaped idea: do a few things deeply and then a lot of things (wide) and later you can deepen those other things."

All existing pages here[https://machinelearning.subwiki.org/wiki/Special:AllPages?fbclid=IwAR0TX3jIjbxD2WB-Vn8hqLkaQ3w2hDUcFyzW48ql9SaovFyzbR6M7cyq4yk]

Red links:

* [[Agglomerative clustering]] ([https://www.youtube.com/watch?v=IUn8k5zSI6g])
* [[Anomaly detection]]
* [[Artificial intelligence]]
* [[Bayesian linear regression]]
* [[Bit]]
* [[Byte]]
* [[Centroid linkage clustering]]
* [[Classification]]
* [[Complete linkage clustering]]
* [[Data mining]]
* [[Evaluation metrics]]
* [[Expert system]]
* [[Hierarchical clustering]]
* [[Machine learning algorithms]]
* [[Machine learning applications]]
* [[Mean linkage clustering]]
* [[Multiple regression]]
* [[Ordinal regression]]
* [[Partitional clustering]]
* [[Poisson regression]]
* [[Reinforcement learning]]
* [[Semi-supervised learning]]
* [[Simple regression]] ([[Simple linear regression]], [[Simple non-linear regression]])
* [[Single linkage clustering]]
* [[Unsupervised learning]] ([[clustering]], [[dimensionality reduction]], [[recommender system]]s, [[deep learning]], [[Density estimation]], [[Market basket analysis]])

* [[Fast forest quantile regression]]
* [[Linear regression]] (expand)
* [[Polynomial regression]]
* [[Lasso regression]]
* [[Stepwise regression]]
* [[Ridge regression]]

* [[Neural network regression]]
* [[Decision forest regression]]
* [[Regression model]]
* [[Boosted decision tree regression]]
* [[Python]]
* [[NumPy]]
* [[scikit-learn]]
* [[Pytorch]]
* [[SciPy]]
* [[matplotlib]]
* [[pandas]]
* [[Dataset]]
* [[Standard deviation]]
* [[TensorFlow]]
* [[Training accuracy]]
* [[Out-of-sample accuracy]]
* [[Procgen Benchmark]]
* [[Optimization]]

* [[Silhouette score]] ([https://www.youtube.com/watch?v=IUn8k5zSI6g])
* [[Soft computing]]

* [[K-nearest neighbor]]

* [[NumPy]] [https://www.coursera.org/lecture/machine-learning-with-python/python-for-machine-learning-WQgHa]
* [[SciPy]] [https://www.coursera.org/lecture/machine-learning-with-python/python-for-machine-learning-WQgHa]
* [[matplotlib]] [https://www.coursera.org/lecture/machine-learning-with-python/python-for-machine-learning-WQgHa]
* [[pandas]] [https://www.coursera.org/lecture/machine-learning-with-python/python-for-machine-learning-WQgHa]
* [[scikit-learn]] [https://www.coursera.org/lecture/machine-learning-with-python/python-for-machine-learning-WQgHa]
* [[Clustering]] [https://www.coursera.org/lecture/machine-learning-with-python/supervised-vs-unsupervised-jgzpX]

User:Sebastian

2022-05-09T04:02:59Z

Unsupervised learning

2022-05-09T03:23:24Z

Sebastian: /* Resources */

2020-04-14T17:10:57Z

Sebastian:

Clustering

2020-04-14T16:27:24Z

Sebastian: /* Motivation */

'''Clustering''' is an [[unsupervised learning]] technique. It is used for grouping data points, or objects that are somehow similar. Clustering means finding clusters in a dataset, unsupervised.<ref name="MLPython">[https://www.coursera.org/learn/machine-learning-with-python/lecture/Nlxjw/intro-to-clustering Intro to Clustering]Coursera</ref>

== Motivation ==

Generally, clustering can be used for one of the following purposes<ref name="MLPython"/>:
* [[Exploratory data analysis]]
* Summary generation
* [[Outlier detection]][https://www.youtube.com/watch?v=hGKY6BAqJ6o]
* Finding duplicates
* Pre-processing step

== Types pof clustering ==

Some divide clustering into two subgroups<ref>[https://www.analyticsvidhya.com/blog/2016/11/an-introduction-to-clustering-and-different-methods-of-clustering/ An Introduction to Clustering and different methods of clustering]analyticsvidhya.com</ref>:
* [[Hard clustering]]: Each data point either belongs to a cluster completely or not.
* [[Soft clustering]]: A probability or likelihood is assigned for putting data points into separate clusters.

== Clustering vs classification ==

== Algorithms ==

Some of the commonly used clustering algorithms are<ref name="pythonistaplanet">[https://pythonistaplanet.com/applications-of-unsupervised-learning/ Real World Applications of Unsupervised Learning]pythonistaplanet.com</ref>:

* [[Partitioned-baseed clustering]]<ref name="MLPython"/>:
** [[K-means clustering|K-means]]:
** [[K-median]]:
** [[Fuzzy c-means]]:
* [[Hierarchical clustering]]<ref name="MLPython"/>:
** [[Agglomerative clustering]]:
** [[Divisive clustering]]:
* [[Density-based clustering]]<ref name="MLPython"/>:
** [[DBSCAN]]:

=== others ===

* [[Expectation maximization]]:
* [[Hierarchical cluster analysis]] (HCA):

== Applications ==

== External links ==

* [https://www.youtube.com/watch?v=xtDMHPVDDKk From Hard to Soft Clustering] [[wikipedia:Pavel A. Pevzner|Pavel A. Pevzner]]

== References ==

* [https://datafloq.com/read/7-innovative-uses-of-clustering-algorithms/6224]
* [https://www.analyticsvidhya.com/blog/2016/11/an-introduction-to-clustering-and-different-methods-of-clustering/]
* [https://www.coursera.org/lecture/ml-foundations/other-examples-of-clustering-cmh30]

== References ==