Your customer provided you with 2. 000 unlabeled records three groups. What is the correct analytical method to use?

Question

Options

• ASemi Linear Regression
• BLogistic regression
• CNaive Bayesian classification
• DLinear regression
• EK-means clustering

• A
  14% (7)
• B
  6% (3)
• C
  4% (2)
• D
  2% (1)
• E
  74% (37)

Explanation

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. The algorithm has nothing to do with and should not be confused with k-nearest neighbor another popular machine learning technique.

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