Suppose that you need to group some data into clusters, and you run K-means 100 times, starting each time from a different random initialisation. You get 100 different clusterings of the data. Which one should you choose? Simply pick one at random. Choose the one that minimises the mean distance of points from the centroid of their allocated cluster. Label the clusters 1, 2, ..., k each time, and assign each point to the cluster it ends up in most often. Choose the clustering you get from the 100th run.

you will learn how to manually classify and cluster data based on a specific variable using R programming language. consists of retrieving data, classifying, plot a graph and interpretation of the outcomes. The dataset that are going to be used in this project is “mtcar” that is available in R studio. Methodology: Using the number of cylinders (cyl column) in mtcar dataset(below), classify cars as 4-, 6- or 8-cylinders car. For each class, give a color to the values that belong to that class. Then plot a graph for using “mpg” column as x-axis and “qsec” column as y-axis.   Steps: Read the file using R programming language. Read values in column “cyl”. If x =8, then paint as red, if x =6, then paint as green, If x =4, then paint as blue. Plot the values of the columns mpg and qsec into an x&y graph. Plot painted values of “cyl” on the same graph. Finally, you have to add your interpretations of the results. This is very important!

Given an unlabeled dataset {x(1),...,x(m)}. The K-mean algorithm has been run with 40 different random initializations, and 40 different clusterings of the data have been Ex0 – µwl² to choose the best obtained. Describe how we should use m m clustering.

Write the generic steps that you will undertake to compute the product of two numbers with 4 digits using Strassen’s algorithm. The 4 digit numbers are A0A1A2A3 and B0B1B2B3. Then compute using Strassen algorithm the product of 27 and 19.

Given numbers = (66, 74, 83, 87, 89, 40, 32, 77), pivot = 32 What is the low partition after the partitioning algorithm is completed? What is the high partition after the partitioning algorithm is completed?

The mechanism of automated scaling has to be mentioned here.

Question 2: The game of "FastestPath" consists of a path (a 1-D array) with n positive integers to represent n cities in a path (except the first index which has always value 0). The aim of the game is to move from the source city (located at index 0) to destination (located at last index) in the shortest time. The value at each index shows the travel time to enter the city located in the corresponding index. Here is a sample path where there are 6 cities (n is 6): 0 5 90 7 61 12 Always start the game from the first city and there are two types of moves. You can either move to the adjacent city or jump over the adjacent city to land two cities over. The total travel time of a game is the sum of the travel times of the visited cities. In the path shown above, there are several ways to get to the end. Starting in the first city, our time so far is 0. We could travel to city 2, then travel to city 4, then travel to last city for a total travel time of 90 + 61 + 12 = 163. However, a…

Q3. Compute the distance vector tables for each node, until they reach a settled state. Present your tables in the style used in the example and shown below, with each iteration appearing in a separate column. [15 marks] Iteration 1 Iteration 2 and so on... cost to Node Node А в с A A C A A 6 A 00 B 00 C 00 00 Node Node A B C D A C D B B A A 00 B 6 1 3 C D I and so on... 8 3. 8 8

Please implement Multinomial Logistic Regression on the following data. Please continue from the given code:

Which statement best describes k-means cluster analysis?   Question 9 options:   It is the process of organizing observations into distinct groups based on a measure of similarity or dissimilarity.   It is the process of agglomerating observations into a series of nested groups based on a measure of similarity or dissimilarity.   It is the process of reducing the number of variables to consider in data-mining.    It is the process of estimating the value of a continuous outcome variable.