Difference between revisions of "Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)"
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| − | [ | + | [https://www.youtube.com/results?search_query=Hierarchical+Agglomerative+Clustering+HAC Youtube search...] |
| − | [ | + | [https://www.google.com/search?q=Hierarchical+Cluster+Agglomerative+Divisive+HDC+Clustering+HAC+learning+ML ...Google search] |
| − | * [[AI Solver]] | + | * [[AI Solver]] ... [[Algorithms]] ... [[Algorithm Administration|Administration]] ... [[Model Search]] ... [[Discriminative vs. Generative]] ... [[Train, Validate, and Test]] |
| − | + | * [[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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* [[Hierarchical Cluster Analysis (HCA)]] | * [[Hierarchical Cluster Analysis (HCA)]] | ||
* [[Hierarchical Temporal Memory (HTM)]] | * [[Hierarchical Temporal Memory (HTM)]] | ||
* [[K-Means]] | * [[K-Means]] | ||
| − | * [ | + | * [https://www.r-bloggers.com/how-to-perform-hierarchical-clustering-using-r/ How to Perform Hierarchical Clustering using R | Perceptive Analytics] |
| − | * [ | + | * [https://www.researchgate.net/publication/315966848_Exploreing_K-Means_with_Internal_Validity_Indexes_for_Data_Clustering_in_Traffic_Management_System Exploreing K-Means with Internal Validity Indexes for Data Clustering in Traffic Management System | S. Nawrin, S. Akhter and M. Rahatur] |
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# Divisive (HDC - DIANA); top-down, first groups all examples into one cluster and then iteratively divides the cluster into a hierarchical tree. | # Divisive (HDC - DIANA); top-down, first groups all examples into one cluster and then iteratively divides the cluster into a hierarchical tree. | ||
| − | + | https://i1.wp.com/r-posts.com/wp-content/uploads/2017/12/Agnes.png | |
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== Agglomerative Clustering - Bottom Up == | == Agglomerative Clustering - Bottom Up == | ||
| − | Bottom-up algorithms treat each data point as a single cluster at the outset and then successively merge (or agglomerate) pairs of clusters until all clusters have been merged into a single cluster that contains all data points. Bottom-up hierarchical clustering is therefore called hierarchical agglomerative clustering or HAC. This hierarchy of clusters is represented as a tree (or dendrogram). The root of the tree is the unique cluster that gathers all the samples, the leaves being the clusters with only one sample. [ | + | Bottom-up algorithms treat each data point as a single cluster at the outset and then successively merge (or agglomerate) pairs of clusters until all clusters have been merged into a single cluster that contains all data points. Bottom-up hierarchical clustering is therefore called hierarchical agglomerative clustering or HAC. This hierarchy of clusters is represented as a tree (or dendrogram). The root of the tree is the unique cluster that gathers all the samples, the leaves being the clusters with only one sample. [https://towardsdatascience.com/the-5-clustering-algorithms-data-scientists-need-to-know-a36d136ef68 The 5 Clustering Algorithms Data Scientists Need to Know | Towards Data Science] |
Hierarchical clustering does not require us to specify the number of clusters and we can even select which number of clusters looks best since we are building a tree. Additionally, the algorithm is not sensitive to the choice of distance metric; all of them tend to work equally well whereas with other clustering algorithms, the choice of distance metric is critical. A particularly good use case of hierarchical clustering methods is when the underlying data has a hierarchical structure and you want to recover the hierarchy; other clustering algorithms can’t do this. These advantages of hierarchical clustering come at the cost of lower efficiency, as it has a time complexity of O(n³), unlike the linear complexity of K-Means and GMM. | Hierarchical clustering does not require us to specify the number of clusters and we can even select which number of clusters looks best since we are building a tree. Additionally, the algorithm is not sensitive to the choice of distance metric; all of them tend to work equally well whereas with other clustering algorithms, the choice of distance metric is critical. A particularly good use case of hierarchical clustering methods is when the underlying data has a hierarchical structure and you want to recover the hierarchy; other clustering algorithms can’t do this. These advantages of hierarchical clustering come at the cost of lower efficiency, as it has a time complexity of O(n³), unlike the linear complexity of K-Means and GMM. | ||
Latest revision as of 21:48, 5 March 2024
Youtube search... ...Google search
- AI Solver ... Algorithms ... Administration ... Model Search ... Discriminative vs. Generative ... Train, Validate, and Test
- Embedding ... Fine-tuning ... RAG ... Search ... Clustering ... Recommendation ... Anomaly Detection ... Classification ... Dimensional Reduction. ...find outliers
- Hierarchical Cluster Analysis (HCA)
- Hierarchical Temporal Memory (HTM)
- K-Means
- How to Perform Hierarchical Clustering using R | Perceptive Analytics
- Exploreing K-Means with Internal Validity Indexes for Data Clustering in Traffic Management System | S. Nawrin, S. Akhter and M. Rahatur
Hierarchical clustering algorithms actually fall into 2 categories:
- Agglomerative (HAC - AGNES); bottom-up, first assigns every example to its own cluster, and iteratively merges the closest clusters to create a hierarchical tree.
- Divisive (HDC - DIANA); top-down, first groups all examples into one cluster and then iteratively divides the cluster into a hierarchical tree.
Agglomerative Clustering - Bottom UpBottom-up algorithms treat each data point as a single cluster at the outset and then successively merge (or agglomerate) pairs of clusters until all clusters have been merged into a single cluster that contains all data points. Bottom-up hierarchical clustering is therefore called hierarchical agglomerative clustering or HAC. This hierarchy of clusters is represented as a tree (or dendrogram). The root of the tree is the unique cluster that gathers all the samples, the leaves being the clusters with only one sample. The 5 Clustering Algorithms Data Scientists Need to Know | Towards Data Science Hierarchical clustering does not require us to specify the number of clusters and we can even select which number of clusters looks best since we are building a tree. Additionally, the algorithm is not sensitive to the choice of distance metric; all of them tend to work equally well whereas with other clustering algorithms, the choice of distance metric is critical. A particularly good use case of hierarchical clustering methods is when the underlying data has a hierarchical structure and you want to recover the hierarchy; other clustering algorithms can’t do this. These advantages of hierarchical clustering come at the cost of lower efficiency, as it has a time complexity of O(n³), unlike the linear complexity of K-Means and GMM.
Divisive Clustering = Top Down
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