When to use hierarchical clustering vs K-means?

A hierarchical clustering is a set of nested clusters that are arranged as a tree. K Means clustering is found to work well when the structure of the clusters is hyper spherical (like circle in 2D, sphere in 3D). Hierarchical clustering don't work as well as, k means when the shape of the clusters is hyper spherical.

What is the advantage of hierarchical clustering compared with K means?

Hierarchical clustering outputs a hierarchy, ie a structure that is more informa ve than the unstructured set of flat clusters returned by k-‐means. Therefore, it is easier to decide on the number of clusters by looking at the dendrogram (see sugges on on how to cut a dendrogram in lab8).

When would you use hierarchical cluster?

Hierarchical clustering is the most popular and widely used method to analyze social network data. In this method, nodes are compared with one another based on their similarity. Larger groups are built by joining groups of nodes based on their similarity.

How does hierarchical clustering differ from K means clustering?

Hierarchical clustering can't handle big data well but K Means clustering can. This is because the time complexity of K Means is linear i.e. O(n) while that of hierarchical clustering is quadratic i.e. O(n²).

When to use K means clustering?

The K-means clustering algorithm is used to find groups which have not been explicitly labeled in the data. This can be used to confirm business assumptions about what types of groups exist or to identify unknown groups in complex data sets.

Question 16 - Compare K Means and Hierarchical Clustering Which is better

What are the limitations of K means clustering?

The most important limitations of Simple k-means are: The user has to specify k (the number of clusters) in the beginning. k-means can only handle numerical data. k-means assumes that we deal with spherical clusters and that each cluster has roughly equal numbers of observations.

Which is not a benefit of k-means?

It requires to specify the number of clusters (k) in advance. It can not handle noisy data and outliers. It is not suitable to identify clusters with non-convex shapes.

What are the advantages of hierarchical clustering?

1) No apriori information about the number of clusters required. 2) Easy to implement and gives best result in some cases. 1) Algorithm can never undo what was done previously. 2) Time complexity of at least O(n² log n) is required, where 'n' is the number of data points.

Is hierarchical clustering slower than non hierarchical clustering?

It is comparatavely more faster than Hierarchical Clustering.

What can we use in hierarchical clustering to find the right number of clusters?

To get the optimal number of clusters for hierarchical clustering, we make use a dendrogram which is tree-like chart that shows the sequences of merges or splits of clusters. If two clusters are merged, the dendrogram will join them in a graph and the height of the join will be the distance between those clusters.

When would you not use hierarchical clustering?

The weaknesses are that it rarely provides the best solution, it involves lots of arbitrary decisions, it does not work with missing data, it works poorly with mixed data types, it does not work well on very large data sets, and its main output, the dendrogram, is commonly misinterpreted.

What are the disadvantages of hierarchical clustering?

Why we use hierarchical clustering What are the step taken to perform hierarchical clustering justify by algorithm?

Hierarchical clustering 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.

When k-means will fail to give good clusters?

K-Means clustering algorithm fails to give good results when the data contains outliers, the density spread of data points across the data space is different and the data points follow non-convex shapes.

What are the pros and cons of the hierarchical clustering?

Which one is better hierarchical or partitioning and why?

Typically, partitional clustering is faster than hierarchical clustering. Hierarchical clustering requires only a similarity measure, while partitional clustering requires stronger assumptions such as number of clusters and the initial centers.

What is the prerequisite for hierarchical clustering?

Space complexity: The space required for the Hierarchical clustering Technique is very high when the number of data points are high as we need to store the similarity matrix in the RAM. The space complexity is the order of the square of n. Space complexity = O(n²) where n is the number of data points.

Is it possible for Kmeans to fail if no assumptions are violated?

k-means assume the variance of the distribution of each attribute (variable) is spherical; all variables have the same variance; the prior probability for all k clusters are the same, i.e. each cluster has roughly equal number of observations; If any one of these 3 assumptions is violated, then k-means will fail.

Is K-means clustering suitable for all shapes and sizes of clusters?

Kmeans assumes spherical shapes of clusters (with radius equal to the distance between the centroid and the furthest data point) and doesn't work well when clusters are in different shapes such as elliptical clusters.

Which of the following is not required by K-means clustering?

Explanation: k-nearest neighbor has nothing to do with k-means.

Is hierarchical clustering is a suggested approach for large data sets?

FALSE Hierarchical clustering is not a suggested approach for large data sets. Hierarchical clustering refers to clustering of any type of data known as ranking. The arrangements are made from either top to bottom or bottom to top. It measures the competency and efficient output of the given data sets.

What does hierarchical clustering tell us?

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.

Is scaling necessary for hierarchical clustering?

It depends on the type of data you have. For some types of well defined data, there may be no need to scale and center. A good example is geolocation data (longitudes and latitudes). If you were seeking to cluster towns, you wouldn't need to scale and center their locations.

What are the two types of hierarchical clustering?

There are two types of hierarchical clustering: divisive (top-down) and agglomerative (bottom-up).