Question

In: Statistics and Probability

Question 1. What is k-means clustering? How does it work? Give a few examples that you...

Question 1.

What is k-means clustering? How does it work? Give a few examples that you would use this algorithm.

----------------

Question 2.

What is k-nearest neighbor? How does it work? Give a few examples that you would use this algorithm.

Expert Solution

SOLUTION1:

K Means Clustering

K means is an iterative clustering algorithm that aims to find local maxima in each iteration. This algorithm works in these 5 steps :

Specify the desired number of clusters K : Let us choose k=2 for these 5 data points in 2-D space.

Randomly assign each data point to a cluster : Let’s assign three points in cluster 1 shown using red color and two points in cluster 2 shown using grey color.

Compute cluster centroids : The centroid of data points in the red cluster is shown using red cross and those in grey cluster using grey cross.

Re-assign each point to the closest cluster centroid : Note that only the data point at the bottom is assigned to the red cluster even though its closer to the centroid of grey cluster. Thus, we assign that data point into grey cluster

Re-compute cluster centroids : Now, re-computing the centroids for both the clusters.

Repeat steps 4 and 5 until no improvements are possible : Similarly, we’ll repeat the 4^th and 5^th steps until we’ll reach global optima. When there will be no further switching of data points between two clusters for two successive repeats. It will mark the termination of the algorithm if not explicitly mentioned.

. Applications of Clustering

Clustering has a large no. of applications spread across various domains. Some of the most popular applications of clustering are:

Recommendation engines
Market segmentation
Social network analysis
Search result grouping
Medical imaging
Image segmentation
Anomaly detection

Solution2:

KNN can be used for both classification and regression predictive problems. However, it is more widely used in classification problems in the industry. To evaluate any technique we generally look at 3 important aspects:

1. Ease to interpret output

2. Calculation time

3. Predictive Power

We were unable to transcribe this image

Applications of KNN Classifier Used in classification Used to get missing values . Used in pattern recognition Used in gene expression Used in protein-protein prediction Used to get 3D structure of protein . Used to measure document similarity

orchestra answered 2 years ago

What is clustering? Explain how K-Means Clustering Algorithm works? What are the Advantages and disadvantages of...

What is clustering? Explain how K-Means Clustering Algorithm works? What are the Advantages and disadvantages of Clustering ALgorithms discussed in our class (K-Means,Hierchal)? Which Clustering Algorithm is better K-Means or hierarchical Clustering? Explain with a proper example which is better algorithm?

Question: In MATLAB, Implement a hybrid clustering algorithm which combines hierarchical clustering and k-means clustering. The...

Question: In MATLAB, Implement a hybrid clustering algorithm which combines hierarchical clustering and k-means clustering. The hybrid algorithm will use hierarchical clustering to produce stable clusters and k-means clustering will initialize seeds based on the centroids of the produced stable clusters (instead of randomly initialized seeds) Background Information: Both hierarchal clustering and k-means clustering group similar data objects into clusters. However, the two algorithms have their pros and cons. For example, hierarchical clustering produces stable clusters while k-means clustering generates...

K-means clustering: a. In the k-means lab, you examined different values for k using the "knee"...

K-means clustering: a. In the k-means lab, you examined different values for k using the "knee" heuristic to pick the best value of k. Explain what is so special about the k values on the “knee”? Hint: There are two properties that together make these values of k special. b. Give an example of a type of data (data type) that k-means should not be used for and explain why.

In MATLAB, Implement a hybrid clustering algorithm which combines hierarchical clustering and k-means clustering.

What dose consequentialism means in few a few examples

In your own words, summarize the steps of K-means clustering. Make sure to give example(s). What...

In your own words, summarize the steps of K-means clustering. Make sure to give example(s). What are the advantages and disadvantages of the K-means clustering? Any limitations?

One way to cluster objects is called k-means clustering. The goal is to find k different...

One way to cluster objects is called k-means clustering. The goal is to find k different clusters, each represented by a "prototype", defined as the centroid of cluster. The centroid is computed as follows: the jth value in the centroid is the mean (average) of the jth values of all the members of the cluster. Our goal is for every member a cluster to be closer to that cluster's prototype than to any of the other prototypes. Thus a prototype...

Try to use K means clustering to segment an image. You can use Matlab function: kmeans(...

Try to use K means clustering to segment an image. You can use Matlab function: kmeans( )

What does the acronym “ADR” stand for, how do ADR’s work; give three examples. Make a...

What does the acronym “ADR” stand for, how do ADR’s work; give three examples. Make a judgment as to the “safety” of Shell’s dividend – is it at risk of being cut, use financial data/results to support your answer.

1. 3 types of machine learn algorithms - regression, clustering, and classification. Please give examples to...

1. 3 types of machine learn algorithms - regression, clustering, and classification. Please give examples to each of these algorithms to explain what business question can be answered by these algorithms. 2. Please describe the overfitting issue in supervised learning, and what method do we usually use to solve it. 3. Describe the definition and difference between supervised and unsupervised learning.