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Hierarchical Clustering

This presentation educates you about Hierarchical Clustering, Clustering, Popular Clustering Algorithms, Hierarchical Clustering Algorithm, Hierarchical Clustering types, Agglomerative Hierarchical Clustering, How does it work?, Linkage Methods, and Divisive Hierarchical Clustering.<br><br>For more topics stay tuned with Learnbay.<br><br>

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Hierarchical Clustering

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  1. Hierarchical Clustering Swipe

  2. Clustering Clustering is a technique that groups similar objects such that the objects in the same group are more similar to each other than the objects in the other groups. The group of similar objects is called a Cluster.

  3. Popular Clustering Algorithms K-Means Clustering Hierarchical Clustering Mean-Shift Clustering Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Expectation-Maximization (EM) Clustering using Gaussian Mixture Models (GMM)

  4. Hierarchical Clustering Algorithm Also called Hierarchical cluster analysis or HCA is an unsupervised clustering algorithm which involves creating clusters that have predominant ordering from top to bottom. For e.g: All files and folders on our hard disk are organized in a hierarchy. The algorithm groups similar objects into groups called clusters. The endpoint is a set of clusters or groups, where each cluster is distinct from each other cluster, and the objects within each cluster are broadly similar to each other.

  5. Hierarchical Clustering types Agglomerative Hierarchical Clustering Divisive Hierarchical Clustering

  6. Agglomerative Hierarchical Clustering The Agglomerative Hierarchical Clustering is the most common type of hierarchical clustering used to group objects in clusters based on their similarity. It’s also known as AGNES (Agglomerative Nesting). It's a “bottom-up” approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy.

  7. How does it work? Make each data point a single-point cluster → forms N clusters Take the two closest data points and make them one cluster → forms N-1 clusters Take the two closest clusters and make them one cluster → Forms N-2 clusters. Repeat step-3 until you are left with only one cluster.

  8. Linkage Methods There are several ways to measure the distance between clusters in order to decide the rules for clustering, and they are often called Linkage Methods. Some of the common linkage methods are: Complete-linkage Single-linkage Average-linkage Centroid-linkage

  9. Divisive Hierarchical Clustering In Divisive or DIANA(DIvisive ANAlysis Clustering) is a top-down clustering method where we assign all of the observations to a single cluster and then partition the cluster to two least similar clusters. Finally, we proceed recursively on each cluster until there is one cluster for each observation. So this clustering approach is exactly opposite to Agglomerative clustering.

  10. There is evidence that divisive algorithms produce more accurate hierarchies than agglomerative algorithms in some circumstances but is conceptually more complex. In both agglomerative and divisive hierarchical clustering, users need to specify the desired number of clusters as a termination condition(when to stop merging).

  11. Topics for next Post Non-Hierarchical clustering ANNOVA ANCOVA Stay Tuned with

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