[amp_mcq option1=”Partitional” option2=”Hierarchical” option3=”Naive bayes” option4=”None of the mentioned” correct=”option1″]<\/p>\n
The correct answer is A. Partitional clustering.<\/p>\n
Partitional clustering is a type of clustering algorithm that divides the data points into a fixed number of clusters. The most common type of partitioning algorithm is k-means clustering, which divides the data points into k clusters such that the sum of the squared distances between each data point and the centroid of its cluster is minimized.<\/p>\n
The figure shows an example of k-means clustering. The data points are represented by circles, and the centroids of the clusters are represented by stars. The data points have been clustered into three clusters, with the centroids of the clusters located at the center of each cluster.<\/p>\n
Hierarchical clustering is a type of clustering algorithm that builds a hierarchy of clusters. The most common type of hierarchical clustering is agglomerative clustering, which starts with each data point in its own cluster and then merges clusters until all of the data points are in one cluster.<\/p>\n
Naive Bayes is a type of supervised learning algorithm that is used for classification. It is based on Bayes’ theorem, which is a mathematical formula that relates the probability of an event to the probability of other events. Naive Bayes assumes that the features of the data are independent, which means that the probability of a data point belonging to a class is not affected by the values of the other features.<\/p>\n
The figure does not show an example of hierarchical clustering or naive Bayes.<\/p>\n","protected":false},"excerpt":{"rendered":"
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