Can I have answer with explanation? 1.(a) What is the main difference between K-means and K-medoids...
Can I have answer with explanation?
1.(a) What is the main difference between K-means and K-medoids
clustering?
(b) What is the main limitation of both K-means and K-medoids
clustering?
Homework Answers
a) Both the k -means and k -medoids algorithms are partitional (breaking the dataset up into groups). 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.
b) K-Means Disadvantages : 1) Difficult to predict K-Value. 2) With global cluster, it didn't work well. 3) Different initial partitions can result in different final clusters. 4) It does not work well with clusters (in the original data) of Different size and Different density
The main disadvantage of K-Medoid algorithms is that it is not
suitable for clustering non-spherical (arbitrary shaped) groups of
objects. ...
It may obtain different results for different runs on the same
dataset because the first k medoids are chosen randomly.
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Can I have answer with explanation?
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Please write full justification for (a) and (b). Will uprate/vote! 4. K-means The goal of K-means clustering is to divide a set of n points into k< n subgroups of points that are "close" t...
Please write full justification for (a) and (b). Will
uprate/vote!
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What does (*) mean in the proof? I keep seeing this and have no idea what it means. It is not in the text. Who ever wrot...
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I need a CLEAR explanation for the answer. I'm confident of the answer I have but...
I need a CLEAR explanation for the answer. I'm
confident of the answer I have but I just want to know
why.
sample correlation between xi and xi is denoted by a. C. Ơ d.ρ ay
Please write full justification for (a) and (b). Will
uprate/vote!
4. K-means The goal of K-means clustering is to divide a set of n points into k< n subgroups of points that are "close" to each other. Each subgroup (or cluster) is identified by the center of the cluster, the centroid (μι, μ2' ··· ,14k) In class, we have seen a brute force approach to solve this problem exactly. Each of the k clusters is represented by a color, e.g.,...
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I need a CLEAR explanation for the answer. I'm
confident of the answer I have but I just want to know
why.
sample correlation between xi and xi is denoted by a. C. Ơ d.ρ ay
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