Databricks Exam Databricks-Certified-Professional-Data-Scientist Topic 3 Question 3 Discussion

Actual exam question for Databricks's Databricks Certified Professional Data Scientist Exam exam

Question #: 3
Topic #: 3

[All Databricks Certified Professional Data Scientist Exam Questions]

Question-18. What is the best way to ensure that the k-means algorithm will find a good clustering of a collection of vectors?

AOnly consider values of k larger than log(N), where N is the number of observations in the data set

BRun at least log(N) iterations of Lloyd's algorithm, where N is the number of observations in the data set

CChoose the initial centroids so that they all He along different axes

DChoose the initial centroids so that they are far away from each other
k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining, k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
The problem is computationally difficult (NP-hard); however there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both algorithms. Additionally, they both use cluster centers to model the data; however k-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes
This Question-is about the properties that make k-means an effective clustering heuristic which primarily deal with ensuring that the initial centers are far away from each other. This is how modern k-means algorithms like k-means++ guarantee that with high probability Lloyd's algorithm will find a clustering within a constant factor of the optimal possible clustering for each k.

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Suggested Answer: D

by Brock at May 04, 2022, 06:05 AM

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