Hierarchical clustering: Difference between revisions
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'''Hierarchical clustering''', also known as hierarchical cluster analysis, is | '''Hierarchical clustering''', also known as hierarchical cluster analysis, is a [[clustering]] algorithm that groups similar objects into groups called clusters.<ref>[https://www.displayr.com/what-is-hierarchical-clustering/ What is Hierarchical Clustering?]displayr.com</ref> Hierarchical clustering is often associated with heatmaps.<ref>[https://www.youtube.com/watch?v=7xHsRkOdVwo StatQuest: Hierarchical Clustering]youtube.com</ref> | ||
== Strategies == | == Strategies == | ||
Strategies for hierarchical clustering generally fall into two types<ref name="intro">[https://www.coursera.org/learn/machine-learning-with-python/lecture/cHku3/intro-to-hierarchical-clustering Intro to Hierarchical Clustering]Coursera</ref>: | Strategies for hierarchical clustering generally fall into two types<ref name="intro">[https://www.coursera.org/learn/machine-learning-with-python/lecture/cHku3/intro-to-hierarchical-clustering Intro to Hierarchical Clustering]Coursera</ref>: | ||
* Divisive: This type works on the assumption that all the feature vectors form a single set and then hierarchically go on dividing this group into different sets.<ref name="ssa">[https://www.sciencedirect.com/topics/computer-science/divisive-clustering Divisive Clustering]sciencedirect.com</ref> | * Divisive: This type works on the assumption that all the feature vectors form a single set and then hierarchically go on dividing this group into different sets.<ref name="ssa">[https://www.sciencedirect.com/topics/computer-science/divisive-clustering Divisive Clustering]sciencedirect.com</ref> | ||
* Agglomerative: In this type partitions are visualized using a tree structure called [[dendrogram]].<ref name="ssa"/> | * Agglomerative: The idea is to ensure nearby points end up in the same cluster.<ref>[https://www.youtube.com/watch?v=XJ3194AmH40 Agglomerative Clustering: how it works]youtube.com</ref> In this type, partitions are visualized using a tree structure called [[dendrogram]].<ref name="ssa"/> | ||
== Advantages vs disadvantages<ref>[https://www.coursera.org/learn/machine-learning-with-python/lecture/h2iAD/more-on-hierarchical-clustering Hierarchical Clustering]Coursera</ref> == | == Advantages vs disadvantages<ref>[https://www.coursera.org/learn/machine-learning-with-python/lecture/h2iAD/more-on-hierarchical-clustering Hierarchical Clustering]Coursera</ref> == | ||
Latest revision as of 15:22, 14 April 2020
Hierarchical clustering, also known as hierarchical cluster analysis, is a clustering algorithm that groups similar objects into groups called clusters.[1] Hierarchical clustering is often associated with heatmaps.[2]
Strategies
Strategies for hierarchical clustering generally fall into two types[3]:
- Divisive: This type works on the assumption that all the feature vectors form a single set and then hierarchically go on dividing this group into different sets.[4]
- Agglomerative: The idea is to ensure nearby points end up in the same cluster.[5] In this type, partitions are visualized using a tree structure called dendrogram.[4]
Advantages vs disadvantages[6]
Advantages
- Hierarchical clustering does not require the number of clusters to be specified.
- It is easy to implement.
- Hierarchical clustering produces a dendogram, which hlps with understanding the data.
Disadvantages
- The hierarchical algorithm can never do any previous steps throughout the algorithm
- The time complexity for the clustering can result in very long computation times in comparison with efficient algorithms such as K-means.
- If we have a large data set, it can become difficult to determine the correct number of clusters by the dendrogram.
References
- ↑ What is Hierarchical Clustering?displayr.com
- ↑ StatQuest: Hierarchical Clusteringyoutube.com
- ↑ Intro to Hierarchical ClusteringCoursera
- ↑ 4.0 4.1 Divisive Clusteringsciencedirect.com
- ↑ Agglomerative Clustering: how it worksyoutube.com
- ↑ Hierarchical ClusteringCoursera