COMPUTER SCIENCE AND ENGINEERING
MACHINE LEARNING
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
|
|
k-mean
|
|
|
Gaussian mixture model
|
|
|
Decision tree
|
|
|
All of the answers
|
Detailed explanation-1: -K-means clustering is the most commonly used clustering algorithm. It’s a centroid-based algorithm and the simplest unsupervised learning algorithm. This algorithm tries to minimize the variance of data points within a cluster.
Detailed explanation-2: -Gaussian mixture models (GMMs) are often used for data clustering. You can use GMMs to perform either hard clustering or soft clustering on query data. To perform hard clustering, the GMM assigns query data points to the multivariate normal components that maximize the component posterior probability, given the data.
Detailed explanation-3: -The GaussianMixture object implements the expectation-maximization (EM) algorithm for fitting mixture-of-Gaussian models.
Detailed explanation-4: -K-Means is a simple and fast clustering method, but it may not truly capture heterogeneity inherent in Cloud workloads. Gaussian Mixture Models can discover complex patterns and group them into cohesive, homogeneous components that are close representatives of real patterns within the data set.
Detailed explanation-5: -The DBSCAN algorithm is a prevalent method of density-based clustering algorithms, the most important feature of which is the ability to detect arbitrary shapes and varied clusters and noise data.