In hierarchical clustering, observations start in their own cluster then observations are iteratively combined based on a specific dissimilarity measurement. Consider the following statements about the agglomerative methods for measuring dissimilarity between observations. Indicate which of the following statements are TRUE in regards to single linkage. (Hint see page • Statement A. Single linkage (nearest neighbor) will consider merging two clusters when an observation in a cluster is close to at least one observation from the other cluster. [Select ] Statement B. Complete linkage (farthest neighbor) merges two clusters if the distance between observations that are most different are relatively close to each other. (Select ) • Statement C. With only two variables, complete linkage may result in long elongated clusters instead of compact circular clusters. [ Select ) • Statement D. Clusters produced by single linkage have approximately equal diameters. I Select ) [ Select )

In hierarchical clustering, observations start in their own cluster then observations are iteratively combined based on a specific dissimilarity measurement. Consider the following statements about the agglomerative methods for measuring dissimilarity between observations. Indicate which of the following statements are TRUE in regards to single linkage. (Hint see page • Statement A. Single linkage (nearest neighbor) will consider merging two clusters when an observation in a cluster is close to at least one observation from the other cluster. [Select ] Statement B. Complete linkage (farthest neighbor) merges two clusters if the distance between observations that are most different are relatively close to each other. (Select ) • Statement C. With only two variables, complete linkage may result in long elongated clusters instead of compact circular clusters. [ Select ) • Statement D. Clusters produced by single linkage have approximately equal diameters. I Select ) [ Select )

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Given are the points A = (3,4), B = (4,4), C = (4,3), D = (-3,6), E = (-4,-3), F = (-3,-3). Starting from initial clusters Cluster1 = {A} which contains only the point A and Cluster2 = {D} which contains only the point D, run the K-means clustering algorithm and report the final clusters. Use L1 distance as the distance between points which is given by d ((x1, y1), (x2, y2)) = | x1 – x2 | + | y1 – y2 |. Draw the points on a 2-D grid and check if the clusters make sense. You may stop the clustering process if it found that two iterations have the same clusters, otherwise, at least 4 iterations are required. Given are the 1-dimensional points A = 2, B = 3, C = 4, D = 9, E = 10, F = 11. Compute complete-linkage hierarchical clustering using d (x, y) = |x – y| as the distance between points. And also draw a dendrogram of it.
Correct answer will be upvoted else Multiple Downvoted. Computer science. You are given one integer n (n>1). Review that a change of length n is a cluster comprising of n unmistakable integers from 1 to n in discretionary request. For instance, [2,3,1,5,4] is a change of length 5, yet [1,2,2] isn't a stage (2 shows up twice in the exhibit) and [1,3,4] is additionally not a change (n=3 but rather there is 4 in the cluster). Your undertaking is to track down a stage p of length n that there is no file I (1≤i≤n) to such an extent that pi=i (along these lines, for all I from 1 to n the condition pi≠i ought to be fulfilled). You need to answer t autonomous experiments. In case there are a few replies, you can print any. It tends to be demonstrated that the appropriate response exists for each n>1. Input The main line of the input contains one integer t (1≤t≤100) — the number of experiments. Then, at that point, t experiments follow. The main line of the experiment…
Correct answer will be upvoted else downvoted. Computer science. It might have been a simple undertaking, yet it worked out that you ought to observe a few guidelines: Before all else, you select any sure integer x. Then, at that point, you do the accompanying activity n times: select two components of cluster with total equivalents x; eliminate them from an and supplant x with limit of that two numbers. For instance, if at first a=[3,5,1,2], you can choose x=6. Then, at that point, you can choose the second and the third components of a with total 5+1=6 and toss them out. After this activity, x equivalents 5 and there are two components in cluster: 3 and 2. You can toss them out on the following activity. Note, that you pick x before the beginning and can't transform it as you need between the activities. Decide how could you act to toss out all components of a. Input The main line contains a solitary integer t (1≤t≤1000) — the number of experiments.…
10. The majority vote rule for the k-nearest neighbours technique can be swapped for a rule that instead takes the fraction of nearest neighbours in each category. What does this fraction give? Group of answer choices The value of k for the next iteration. The new number of categories. The new cluster centroid position. The probability of belonging to each category.
Consider a set of data points {(-10, 13), (-6, 9), (-6, 17), (-2, 13), (-3, 0)}. Add last two digits of your registration with each given value such as. If last two digit of your registration number is 14 then First point will be (-10+14, 13+14) = (4, 27). Use the k-means clustering procedure with k = 2 to groups these data points. Start with the three initial cluster centers: (-6,9), (-2,13), (10,15). Show your working for 2 iterations. Use D=√(xi-yi)*2 for distance calculation.
Suppose that the data mining task is to cluster the following eight points (with (x, y)representing location) into three clusters:(3, 10), (2, 15), (8, 10), (5, 8), (4, 5), (6, 4), (3, 2), (4, 12)The distance function is Euclidean distance. Suppose initially we assign A1, B1, and C1 as the center of each cluster, respectively. Use the k-means algorithm to show onlya. The three cluster centers after the first round execution.b. The three clusters after the first round execution.
What are some of the strengths of Hierarchical Clustering ? Group of answer choices a. We do not have to assume any particular number of clusters from the beginning. b. Time complexity is low and the algorithm runs quickly irrespective of dataset size. c. Insenstitive to noise and outliers regardless of type of linkage (eg. Single, Complete etc.) d. Easy to implement and can produce an ordering of objects, which may be informative for the display.
Correct answer will be upvoted else Multiple Downvoted. Don't submit random answer. Computer science. each individual has a rating chart portrayed by a variety of integers an of length n. You are currently refreshing the foundation, so you've made a program to pack these diagrams. The program functions as follows. Given an integer boundary k, the program takes the base of each adjoining subarray of length k in a. All the more officially, for a cluster an of length n and an integer k, characterize the k-pressure exhibit of an as a cluster b of length n−k+1, to such an extent that bj=minj≤i≤j+k−1ai For instance, the 3-pressure cluster of [1,3,4,5,2] is [min{1,3,4},min{3,4,5},min{4,5,2}]=[1,3,2]. A stage of length m is an exhibit comprising of m unmistakable integers from 1 to m in subjective request. For instance, [2,3,1,5,4] is a stage, however [1,2,2] isn't a change (2 shows up twice in the exhibit) and [1,3,4] is likewise not a stage (m=3 but rather there is 4 in the…
Suppose we cluster a set of N data points using two different using the k-means clusteringalgorithm runs but with different number of initial clusters centres.Run 1: 4 initial cluster centres - (a,b), (c,d), (e,f) and (g,h). Run 2: 2 initial cluster centres - (a,b), (c,d) Run 3: 3 initial cluster centres - ((a,b), (c,d), (e,f). Would the clusters created using Run 3 be similar to the 2 or 4 initial cluster runs? Explainyour answer briefly.
3.. Based on Euclidean distance in R, we obtained following three clusters: A =(0,5,6),B =(3,9),C =(12). In the next iteration of the clustering, which two clusters will be merged by complete linkage and single linkage approaches, respectively ?
Correct answer will be upvoted else downvoted. Computer science. You are given an integer n and a cluster a1,a2,… ,an. You should reorder the components of the cluster an in such manner that the amount of MEX on prefixes (I-th prefix is a1,a2,… ,computer based intelligence) is expanded. Officially, you should find an exhibit b1,b2,… ,bn, to such an extent that the arrangements of components of clusters an and b are equivalent (it is comparable to cluster b can be found as a cluster a with some reordering of its components) and ∑i=1nMEX(b1,b2,… ,bi) is expanded. MEX of a bunch of nonnegative integers is the insignificant nonnegative integer to such an extent that it isn't in the set. For instance, MEX({1,2,3})=0, MEX({0,1,2,4,5})=3. Input The principal line contains a solitary integer t (1≤t≤100) — the number of experiments. The principal line of each experiment contains a solitary integer n (1≤n≤100). The second line of each experiment contains n integers…
Imagine a database that is structured like a tree with its roots at the top. Imagine for a moment that we decided to add a fake vertex in between each pair of existing vertices. Demonstrate that adhering to the tree protocol on the new tree yields superior concurrent results to adhering to the tree protocol on the initial tree.