Explain the concept of hierarchical clustering. Compare and contrastagglomerative and divisive hierarchical clustering. Discuss theapplications of hierarchical clustering in real-world situations.
Question
Explain the concept of hierarchical clustering. Compare and contrastagglomerative and divisive hierarchical clustering. Discuss theapplications of hierarchical clustering in real-world situations.
Solution
Hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. It is an algorithm that starts by treating each observation as a separate cluster. Then, it repeatedly executes the following two steps: (1) identify the two clusters that are closest together, and (2) merge the two most similar clusters. This iterative process continues until all the clusters are merged together. This is called agglomerative because it merges clusters iteratively.
On the other hand, divisive hierarchical clustering is exactly the opposite of agglomerative hierarchical clustering. It starts with all the observations in a large cluster and breaks it down into smaller pieces. These methods continue this iterative process until each cluster only contains a single observation.
In terms of comparison, agglomerative is a "bottom up" approach where clustering starts with individual points and each step merges the closest clusters until only one cluster (or k clusters) left. On the other hand, divisive is a "top down" approach where all observations start in one cluster and splits are performed recursively as one moves down the hierarchy.
In real-world situations, hierarchical clustering is used in many fields such as bioinformatics, where it is used to group similar genes with similar functions that respond to different conditions. In the field of marketing, it can be used to segment the target market into multiple groups to perform market analysis. In the field of social networking, hierarchical clustering can be used to group similar activities or interests into clusters for recommendation systems.
Similar Questions
What is a key characteristic of hierarchical clustering?Answer areaIt requires the number of clusters to be specified in advanceIt can be visualized using a dendrogramIt is a partitional clustering methodIt is always faster than K-Means
Which of the following is a type of hierarchical clustering?Answer areaK-MeansDBSCANAgglomerative clusteringMean Shift
Hierarchical Clustering
Hierarchical clustering could be a natural next step for further analysis, especially considering the presence of the outliers and the potentially uneven cluster shapes observed in the KMeans model results. Hierarchical clustering can offer insights into the hierarchical structure of the data, potentially capturing nuances that KMeans might overlook.Which of the following is a key difference between KMeans and Hierarchical clustering algorithms?Hierarchical Clustering forms spherical clusters around centroids. Each data point belongs to the cluster with the nearest centroid. In contrast, KMeans builds a hierarchy of clusters, starting with individual data points and gradually merging them into larger clusters based on proximity.Hierarchical clustering assumes clusters to be spherical and of equal size, which may not always reflect the true underlying structure of the data, whereas KMeans can capture clusters of arbitrary shapes and sizes, offering more flexibility in cluster formation.Hierarchical Clustering requires the number of clusters (K) to be specified in advance. In contrast, KMeans does not require the number of clusters to be predefined. It produces a hierarchical tree of clusters, and the number of clusters can be determined after the clustering process.Hierarchical clustering does not require the number of clusters to be predefined. It produces a hierarchical tree of clusters, and the number of clusters can be determined after the clustering process, whereas KMeans requires the number of clusters (K) to be specified in advance.
Example 10.3 Agglomerative versus divisive hierarchical clustering. Figure 10.6 shows the appli- cation of AGNES (AGglomerative NESting), an agglomerative hierarchical clustering method, and DIANA (DIvisive ANAlysis), a divisive hierarchical clustering method, on a data set of five objects, {a, b, c, d, e}. Initially, AGNES, the agglomerative method, places each object into a cluster of its own. The clusters are then merged step-by-step according to some criterion. For example, clusters C1 and C2 may be merged if an object in C1 and an object in C2 form the minimum Euclidean distance between any two objects from solve
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