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K-Means Clustering is a technique most suitable for identifying underlying patterns in high-dimensional data without the need for explicit labeling. It groups data into clusters based on similarity, where each cluster represents a pattern or structure in the data. K-Means is useful for exploratory data analysis to discover patterns or groupings within unlabelled data. Why Other Options are Wrong: a) Principal Component Analysis (PCA) reduces dimensionality but does not identify patterns or groupings. b) Linear Regression is a supervised learning technique used for predicting continuous values rather than identifying patterns in unlabelled data. d) Decision Trees are used for classification or regression tasks and require labelled data. e) Naive Bayes Classifier is a classification algorithm that also requires labelled data and does not identify patterns in unlabelled datasets.
Select the set of numbers that is not similar to the following set.
(14, 48, 72)
Four numbers have been given, out of which three are alike in a certain way and one is different. Select the one that is different.
Odd one out
In the following question, select the odd word from the given alternatives.
In the following question, select the odd letters from the given alternatives.
Odd one out
Three of the following words are alike in some manner and hence form a group. Which word does NOT belong to that group? (The words must be considered a...
In the following question, select the option which is different from the other three options.
Find the odd one out.
Find the odd one out of the choices given therein.