Sage-Tips

Why K means clustering is better than hierarchical?

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 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 disadvantages of K means clustering?

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 is the disadvantage of 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 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.

What are the pros and cons of the k-means algorithm?

1) If variables are huge, then K-Means most of the times computationally faster than hierarchical clustering, if we keep k smalls. 2) K-Means produce tighter clusters than hierarchical clustering, especially if the clusters are globular. K-Means Disadvantages : 1) Difficult to predict K-Value.

What are the advantages of K Medoids over k-means?

“It [k-medoid] is more robust to noise and outliers as compared to k-means because it minimizes a sum of pairwise dissimilarities instead of a sum of squared Euclidean distances.” Here’s an example: Suppose you want to cluster on one dimension with k=2.

What are some disadvantages of K-means that are overcome by DBScan?

Disadvantages of K-Means

• Sensitive to number of clusters/centroids chosen.
• Does not work well with outliers.
• Gets difficult in high dimensional spaces as the distance between the points increases and Euclidean distance diverges (converges to a constant value).
• Gets slow as the number of dimensions increases.

What are pros and cons of K means algorithm?

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.

How do you overcome the disadvantages of K means clustering?

One way to overcome those weaknesses is to use K-mean clustering only if there are available many data. To overcome outliers problem, we can use median instead of mean. Some people pointed out that K means clustering cannot be used for other type of data rather than quantitative data.