What is the time complexity of K means?

Abstract: The k-means algorithm is known to have a time complexity of O(n 2 ), where n is the input data size. This quadratic complexity debars the algorithm from being effectively used in large applications.

Which one is the biggest drawback of K means?

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.

What is the space complexity of K means algorithm?

The space complexity of K-means clustering algorithm is O(N(D + K)). Based on the number of distance calculations, the time complexity of K-means is O(NKI).

Which is faster K means or K Medoids?

K means is quite fast and less expensive than k medoid. K medoid computes all the pairwise distances, it is O(n^2*k*i), k-means runs in O(n*k*i), k times the number of iterations is k*i << n. Hope this answer helps.

What does K refers in the K-Means algorithm Mcq?

K-means is extremely sensitive to cluster center initializations2. Bad initialization can lead to Poor convergence speed3. Bad initialization can lead to bad overall clustering.

Which statement is true about the K-Means algorithm?

Answer: K-means clustering is one of the simplest and popular unsupervised machine learning algorithms. In other words, the K-means algorithm identifies k number of centroids, and then allocates every data point to the nearest cluster, while keeping the centroids as small as possible.

Why k-means fail?

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.

What are disadvantages of k-means?

K-Means Clustering Algorithm has the following disadvantages- 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.

How k-means is different from k-medoids?

K-means attempts to minimize the total squared error, while k-medoids minimizes the sum of dissimilarities between points labeled to be in a cluster and a point designated as the center of that cluster. In contrast to the k -means algorithm, k -medoids chooses datapoints as centers ( medoids or exemplars).

What does K refers in the K-means algorithm a complexity B fixed value C No of iterations D number of clusters?

8 observations are clustered into 3 clusters using K-Means clustering algorithm….