Explain what k-means clustering is and its role in the overall clustering concept.
Explain what k-means clustering is and its role in the overall clustering concept.
5. Hierarchical clustering and k-means clustering both require the mumber of clusters (k) to be specified in advance False True Explain
5. Hierarchical clustering and k-means clustering both require the mumber of clusters (k) to be specified in advance False True Explain
What are some strengths and weaknesses of hierarchical clustering compared to k-means clustering?
What is the difference between KNN and k-means clustering? Write in detail.
Explain the k-means clustering algorithm. Give a precise description. Can k-means ever give results which contain more or less than k clusters?
write a matlab code to compare K Means, Mean shift and Fuzzy C clustering algorithms using images
write a matlab code to compare K Means, Mean shift and Fuzzy C clustering algorithms using images
You have performed an unsupervised k-means clustering on a data set with two attributes and the results indicate a k of 2. Later, you determine the class values for each data instance (there are four class values) and a supervised clustering results in a k of 4. Provide a possible explanation for why the two clustering methods disagree on a k value and a draw a sketch of the two clusterings to go along with your explanation.
k-Means clustering method assigns observations to groups based on their distance to the center of the whole dataset. T?F?
Suppose you have been building a model using the k-means clustering algorithm and you keep finding that a certain variable is essentially ignored by the model (in other words, the variable is very similarly distributed across all clusters). Describe a method that can be used to exaggerate or minimize the impact of a variable when using k-means clustering. Why does this method work?
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?
a) Why is implementing a K-means clustering algorithm multiple times with a fixed K important to do? 119 b) Why is cross-validation preferred over resubstituting as a method to measure classification accuracy? Explain c) Give two situations when nearest neighbor classification may be preferred over linear and quadratic discriminant analysis methods in general. Explain your answer.
a) Why is implementing a K-means clustering algorithm multiple times with a fixed K important to do? 119 b) Why is cross-validation preferred over...