Why k-means is better than hierarchical 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).

What are the weaknesses of hierarchical clustering?

Limitations of Hierarchical Clustering

• Sensitivity to noise and outliers.
• Faces Difficulty when handling with different sizes of clusters.
• It is breaking large clusters.
• In this technique, the order of the data has an impact on the final results.

How can k-means be used for hierarchical clustering?

In hierarchical k-means we pick some k to be the branching factor. This defines the number of clusters Figure 2: An example of k = 3 means hierarchical clustering. First sort the points into clusters and then recursively cluster each clustered set of points. at each level of the clustering hierarchy.

What are the main weaknesses 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.

Should I use K means or hierarchical clustering?

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.

How do you choose between K means and hierarchical clustering?

K-Means vs Hierarchical

1. If there is a specific number of clusters in the dataset, but the group they belong to is unknown, choose K-means.
2. If the distinguishes are based on prior beliefs, hierarchical clustering should be used to know the number of clusters.
3. With a large number of variables, K-means compute faster.

What are the pros and cons of hierarchical clustering?

There’s a lot more we could say about hierarchical clustering, but to sum it up, let’s state pros and cons of this method:

• pros: sums up the data, good for small data sets.
• cons: computationally demanding, fails on larger sets.

What are the strengths and weaknesses of hierarchical clustering?

What are the Strengths and Weaknesses of Hierarchical Clustering?

• Easy to understand and easy to do…
• …
• Arbitrary decisions.
• Missing data.
• Data types.
• Misinterpretation of the dendrogram.
• There are better alternatives.

When should we use hierarchical clustering?

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.

What are some shortcomings of K-means and hierarchical clustering?

K-Means Disadvantages :

• Difficult to predict K-Value.
• With global cluster, it didn’t work well.
• Different initial partitions can result in different final clusters.
• It does not work well with clusters (in the original data) of Different size and Different density.

What are the pros and cons having higher K value?

k-Means Advantages and Disadvantages

• Relatively simple to implement.
• Scales to large data sets.
• Guarantees convergence.
• Can warm-start the positions of centroids.
• Easily adapts to new examples.
• Generalizes to clusters of different shapes and sizes, such as elliptical clusters.
• Choosing manually.

Which clustering algorithm is best?

The Top 5 Clustering Algorithms Data Scientists Should Know

• K-means Clustering Algorithm.
• Mean-Shift Clustering Algorithm.
• DBSCAN – Density-Based Spatial Clustering of Applications with Noise.
• EM using GMM – Expectation-Maximization (EM) Clustering using Gaussian Mixture Models (GMM)
• Agglomerative Hierarchical Clustering.

What is hybrid Hierarchical k-means clustering (hkmeans)?

The result might be (slightly) different each time you compute k-means. To avoid this, a solution is to use an hybrid approach by combining the hierarchical clustering and the k-means methods. This process is named hybrid hierarchical k-means clustering (hkmeans).

What are the limitations of k-means clustering?

However, it has some limitations: it requires the user to specify the number of clusters in advance and selects initial centroids randomly. The final k-means clustering solution is very sensitive to this initial random selection of cluster centers.

Is hierarchical clustering expensive in terms of time complexity?

As hierarchical clustering is very expensive in terms of time complexity (though it provides better result), I have designed my clustering framework as follows: do K-means clustering to partition the data into several bins (k is unknown so I make it reasonably large. eg. k=500)

What are the different types of clustering algorithms?

The most popular clustering algorithms are: k-means clustering, a partitioning method used for splitting a dataset into a set of k clusters. hierarchical clustering, an alternative approach to k-means clustering for identifying clustering in the dataset by using pairwise distance matrix between observations as clustering criteria.