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 3 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.

INTRODUCTION TO DATA MINING Question 3: K-means clustering Use the k-means algorithm and Euclidean distance to...

INTRODUCTION TO DATA MINING Question 3: K-means clustering Use the k-means algorithm and Euclidean distance to cluster the following seven examples into two clusters: A1=(1, 1), A2=(1.5, 2), A3=(3,4), A4=(5,7), A5=(3.5,5), A6=(4.5,5), A7=(3.5,4.5) Suppose that the initial seeds (centers of each cluster) are A1 and A4. Run the k-means algorithm for 2 epochs. At the end of this epoch show: a) Distance matrix by calculating Euclidean distance. b) The new clusters (i.e. the examples belonging to each cluster) c) The...

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?

Data mining--> Please Perform Principal Component Analysis and K-Means Clustering on the Give dataset Below. [50...

Data mining--> Please Perform Principal Component Analysis and K-Means Clustering on the Give dataset Below. [50 Points] Dataset Link : https://dataminingcsc6740.s3-us-west-2.amazonaws.com/datasets/homework_2.csv 10 Points for Data Preprocessing. 15 Points for PCA Algorithm along with plots and Results Explaination. 15 Points for K-Means Algorithm with plots and Results Explination. 10 Points for Comparing the results between PCA and K-Means and whats your infer- ence from your ouputs of the algorithms. Hints: As per the data preprocessing step convert all the variables in...

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...

Suppose you have been building a model using the k-means clustering algorithm and you keep finding...

Suppose you have been building a model using the k-means clustering algorithm and you keep finding that a certain variable is essentially ignored by the model (in other words, the variable is very similarly distributed across all clusters). Describe a method that can be used to exaggerate or minimize the impact of a variable when using k-means clustering. Why does this method work? no additional info available, predictive analysis