which statement is not correct? group of answer choices it is easy to implement k-means and identify unknown groups of data from complex data sets k-means and knn are two names for same algorithm k-means measure the euclidean distance between records in the training data set k-means lacks consistency

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12-12-2022
Computers and Technology

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which statement is not correct? group of answer choices it is easy to implement k-means and identify unknown groups of data from complex data sets k-means and knn are two names for same algorithm k-means measure the euclidean distance between records in the training data set k-means lacks consistency

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tutorconsortium111 tutorconsortium111 · Answer 1 · 2022-12-18T10:00:13+01:00

It is easy to implement k-means lacks consistency

What is euclidean distance?

Euclidean distance is a measure of the straight-line distance between two points in Euclidean space. It is the most widely used method of measuring distance between two points, and is the basis for many other types of distance metrics. Euclidean distance is calculated as the square root of the sum of the squared differences between a pair of points. For example, the Euclidean distance between points (x1,y1) and (x2,y2) is given by the formula: √(x2-x1)2 + (y2-y1)2. Euclidean distance can be calculated in any number of dimensions, and is particularly useful when dealing with multidimensional data. It is often used in machine learning and data mining applications to measure the similarity between data points.

To learn more about euclidean refer to:

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