Identify the clustering method which takes care of variance in data(1 Point)Decision treeGaussian mixture modelK meansAll of the above
Question
Identify the clustering method which takes care of variance in data(1 Point)Decision treeGaussian mixture modelK meansAll of the above
Solution
The clustering method which takes care of variance in data is the Gaussian Mixture Model. This model is a probabilistic model that assumes all the data points are generated from a mixture of a finite number of Gaussian distributions with unknown parameters. It is capable of incorporating the covariance between the points into the model to identify more complex clusters. Unlike K-means which assumes that each cluster's variance is spherical and equal, the Gaussian Mixture Model takes into account the variance of the data. Decision trees, on the other hand, are typically used for classification problems rather than clustering.
Similar Questions
Which of the following is an example of a clustering algorithm?(1 Point)Decision treeRandom forestK-meansGradient descent
How does the k-means algorithm determine which data points belong to which cluster?Select one:a.By evaluating the variance of each clusterb.By evaluating the probability that a data point belongs to each clusterc.By comparing the data point to the characteristics of each clusterd.By computing the distance between data points and the centroid of each cluster
What is a decision tree algorithm used for?1 pointa. Classificationb. Regressionc. Clusteringd. Dimensionality reduction
K means clustering algorithm clusters the data points based on:- Dependent and independent variables The eigen values Distance between the points and a cluster centre None of the above
How is the final set of clusters determined in the k-means algorithm?Select one:a.By selecting the set of clusters that minimize the sum of squared errorsb.By selecting the set of clusters that maximize the within-cluster variancec.By selecting the set of clusters that maximize the sum of squared errorsd.By selecting the set of clusters that minimize the within-cluster variance
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