Difference between revisions of "Clustering"

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[https://www.youtube.com/results?search_query=~Clustering+AI YouTube]
 
[https://www.youtube.com/results?search_query=~Clustering+AI YouTube]
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[https://www.bing.com/news/search?q=~Clustering+AI&qft=interval%3d%228%22 ...Bing News]
 
[https://www.bing.com/news/search?q=~Clustering+AI&qft=interval%3d%228%22 ...Bing News]
  
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* [[...cluster]] - [[AI Solver]]
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* [[Embedding]] ... [[Fine-tuning]] ... [[Retrieval-Augmented Generation (RAG)|RAG]] ... [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]]. [[...find outliers]]
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* [[Embedding]]:  [[Agents#AI-Powered Search|Search]] ... [[Clustering]] ... [[Recommendation]] ... [[Anomaly Detection]] ... [[Classification]] ... [[Dimensional Reduction]] ... [[...find outliers]]
 
 
** [[Singular Value Decomposition (SVD)]]
 
** [[Singular Value Decomposition (SVD)]]
 
** [[Principal Component Analysis (PCA)]]
 
** [[Principal Component Analysis (PCA)]]
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** [[Variational Autoencoder (VAE)]]
 
** [[Variational Autoencoder (VAE)]]
 
** [[Biclustering]]
 
** [[Biclustering]]
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** [[OPTICS: Ordering Points To Identify the Clustering Structure]]
 
** [https://en.wikipedia.org/wiki/Multidimensional_scaling Multidimensional Scaling (MDS)]
 
** [https://en.wikipedia.org/wiki/Multidimensional_scaling Multidimensional Scaling (MDS)]
 
** Hierarchical; to include clustering  
 
** Hierarchical; to include clustering  
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*** [[Hierarchical Clustering;  Agglomerative (HAC) & Divisive (HDC)]]
 
*** [[Hierarchical Clustering;  Agglomerative (HAC) & Divisive (HDC)]]
 
*** [[Hierarchical Temporal Memory (HTM)]]
 
*** [[Hierarchical Temporal Memory (HTM)]]
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* [[Excel]] ... [[LangChain#Documents|Documents]] ... [[Database|Database; Vector & Relational]] ... [[Graph]] ... [[LlamaIndex]]
  
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Similarity Measures for Clusters:
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= Similarity Measures for Clusters =
 
* Compare the numbers of identical and unique item pairs appearing in cluster sets
 
* Compare the numbers of identical and unique item pairs appearing in cluster sets
 
* Achieved by counting the number of item pairs found in both clustering sets (a) as well as the pairs appearing only in the first (b) or the second (c) set.
 
* Achieved by counting the number of item pairs found in both clustering sets (a) as well as the pairs appearing only in the first (b) or the second (c) set.
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[https://girke.bioinformatics.ucr.edu/GEN242/mydoc_Rclustering_3.html#example-2 Clustering Algorithms | Data Analysis in Genome Biology]
 
[https://girke.bioinformatics.ucr.edu/GEN242/mydoc_Rclustering_3.html#example-2 Clustering Algorithms | Data Analysis in Genome Biology]
  
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= OPTICS: ordering points to identify the clustering structure =
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= Unsupervised Learning =
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* [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]
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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).
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== News Headlines With Text Clustering ==  
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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.
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<youtube>23qfPq0m7XA</youtube>
  
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Cluster analysis is a primary method for database mining. It is either used as a stand-alone tool to get insight into the distribution of a data set, e.g. to focus further analysis and data processing, or asa preprocessing step for other algorithms operating on the detected clusters. Almost all of the well-known clustering algorithms require input parameters which are hard to determine but have a significant influence on the clustering result. Furthermore, for many real-datasets there does not even exist a global parameter setting for which the result of the clustering algorithm describes the intrinsic clustering structure accurately. We introduce a new algorithm for the pur-pose of cluster analysis which does not produce a clustering of a data set explicitly; but instead creates an augmented ordering of the database representing its density-based clustering structure. This cluster-ordering contains information which is equivalent to the density-based clusterings corresponding to a broad range of parameter settings. It is a versatile basis for both automatic and interactive cluster analysis. We show how to automatically and efficiently extract not only ‘traditional’ clustering information (e.g. representative points, arbitrary shaped clusters), but also the intrinsic clustering structure. For medium sized data sets, the cluster-ordering can be represented graphically and for very large data sets, we introducean appropriate visualization technique. Both are suitable for inter-active exploration of the intrinsic clustering structure offering additional insights into the distribution and correlation of the data.
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= Feature Extraction =
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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).
  
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In this paper, we proposed a cluster analysis method based on the OPTICS algorithm. OPTICS computes an augmented cluster-ordering of the database objects. The main advantage of ourapproach, when compared to the clustering algorithms pro-posed in the literature, is that we do not limit ourselves to oneglobal parameter setting. Instead, the augmented cluster-order-ing contains information which is equivalent to the density-based clusterings corresponding to a broad range of parameter settings and thus is a versatile basis for both automatic and interactive cluster analysis. We demonstrated how to use it as a standalone tool to get in-sight into the distribution of a data set. Depending on the size of the database, we either represent the cluster-ordering graphically (for small data sets) or use an appropriate visualization technique (for large data sets). Both techniques are suitable for interactively exploring the clustering structure, offering additional insights into the distribution and correlation of the data. We also presented an efficient and effective algorithm to auto-matically extract not only ‘traditional’ clustering information but also the intrinsic, hierarchical clustering structure. There are several opportunities forfuture research. For very high-dimensional spaces, no index structures exist to efficiently support the hypersphere range queries needed by the OPTICS algorithm. Therefore it is infeasible to apply it in its current form to a database containing several million high-dimensional objects. Consequently, the most interesting question is whether we can modify OPTICS so that we can trade-off a limited amount of accuracy for a large gain in efficiency. Incrementally managing a cluster-ordering when updates on the database oc-cur is another interesting challenge. Although there are techniques to update a ‘flat’ density-based decomposition[EKS+ 98] incrementally, it is not obvious how to extend these ideas to a density-based cluster-ordering of a data set.
 
  
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Latest revision as of 13:13, 16 September 2023

YouTube ... Quora ...Google search ...Google News ...Bing News

Similarity Measures for Clusters

  • Compare the numbers of identical and unique item pairs appearing in cluster sets
  • Achieved by counting the number of item pairs found in both clustering sets (a) as well as the pairs appearing only in the first (b) or the second (c) set.
  • With this a similarity coefficient, such as the Jaccard index, can be computed. The latter is defined as the size of the intersect divided by the size of the union of two sample sets: a/(a+b+c).
  • In case of partitioning results, the Jaccard Index measures how frequently pairs of items are joined together in two clustering data sets and how often pairs are observed only in one set.
  • Related coefficient are the Rand Index and the Adjusted Rand Index. These indices also consider the number of pairs (d) that are not joined together in any of the clusters in both sets

Clustering Algorithms | Data Analysis in Genome Biology


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

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).


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