Suppose we have three cluster centroids pµl=[1,2], µ2=[-3,0] and µ3=[4,2]. Furthermore, we have a training example x(i)=[-1, 2]. After a cluster assignment step, which cluster will v/i) helongto- cd1) c2) or c/31?

Suppose we have three cluster centroids pµl=[1,2], µ2=[-3,0] and µ3=[4,2]. Furthermore, we have a training example x(i)=[-1, 2]. After a cluster assignment step, which cluster will v/i) helongto- cd1) c2) or c/31?

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

See similar textbooks

Related questions

Q: Review that a change of length n is a cluster comprising of n unmistakable integers from 1 to n in…

A: Here have to determine about the Special Permutation programming problem statement.

Q: Think for a Kafka cluster with: two Brokers: B1 and B2, and two Topics: T1 (with one partition P1)…

Q: Let D be a dataset of two-dimensional points to be partitioned into 5 clusters. After applying two…

A: # required modules import numpy as np import matplotlib.pyplot as plt import pandas as pd #…

Q: What is clustering, define k-means clustering, its algorithm, explain complexity and why it always…

A: What is clustering: A cluster refers to a collection of data points aggregated together because of…

Q: to group these data points. Start with the three initial cluster centers: (-6, 9, 5), (-2, 13, 10),…

A: Consider a set of data points {(7, 30, 27), (11, 26, 22), (11, 34, 26), (15, 30, 27), (14, 17, 22),…

Q: Apply the k-means algorithm for the dataset A ∪ B = {−5, −3, 2, 4} with initial cluster centers {2,…

A: k-meаns сlustering is а methоd оf veсtоr quаntizаtiоn, оriginаlly frоm signаl рrосessing,…

Q: Suppose we cluster a set of N data points using two different using the k-means clustering algorithm…

A: Suppose we cluster a set of N data points using two different using the k-means clusteringalgorithm…

Q: Assume we have a 20 dataset consisting of (0, –6), (4, 4), (0,0) , (–5, 2). We wish to do k-means…

Q: can play out the accompanying activity: pick some subarray (adjoining subsegment) of an and improve…

A: Here have to determine about pick some subarray problem statement.

Q: Question 7. Is the following network structurally balanced? Explain your answer. If it is…

A: Structurally balanced graph: It is used to form a triangle, rectangle, pentagon shapes to merge…

Q: Given a set of three points, -2, 0, and 9, we want to use k- Means Clustering with k = 2 to cluster…

A: K-means clustering works as choosing k clusters randomly , then adds the nearest elements to the…

Q: Assume, you want to cluster 8 observations into 3 clusters using K-Means clustering algorithm. After…

A: Here we have given solution on the Euclidean distance for observation (9, 9) from cluster centroid…

Q: Suppose that D = (0 3 5 7; 3 0 4 4; 5 4 0 1; 7 41 0) is a distance matrix for 4 points, and you have…

A: UPGMA stands for Unweighted Pair Group method with Arithmetic Mean. Given: D =…

Q: Question: If there are n observations in the data set, after how many iterations will the…

A: A task involving machine learning may not be queue, but has a number of known steps:Description of…

Q: Question # 2. Clustering CLO3,4, PLO-1,2,3, C-1,3 16 Let’s suppose, there are seven data points…

Q: Question No. 3: a) Given are the points A = (3,4), B = (4,4), C = (4,3), D = (-3,6), E = (-4,-3), F…

A: Given data is shown below: Question No. 3: a) Given are the points A = (3,4), B = (4,4), C =…

Q: Given the compatibility graph shown in Figure Q3, execute the cluster-partitioning algorithm to…

A: Here below i amusing partitioning algorithm for find minimal number of clusters:…

Q: True or false: when using k-means clustering, the final clusters are highly dependent on the initial…

A: The answer is given below:

Q: Given are the points A = (3,4), B = (4,4), C = (4,3), D = (-3,6), E = (-4,-3), F = (-3,-3). Starting…

A: Lets see the solution.

Q: Provide brief answers with supporting computations: a. Given below are clustering results of…

A: A simple quality check on the cluster is to find the difference between the proportion of items of…

Q: use complete-link agglomerative clustering to cluster the following 1-dimensional data…

A: complete-link agglomerative clustering cluster the following 1-dimensional data…

Q: giving a cluster is excessively normal. Rather than that, he chose to gift Mila the portion needs…

A: In this question, we are asked to print the maximum value of k at the end of program Algorithm: 1)…

Q: two clusters would be merged first in agglomerative clustering using a Consider the three clusters…

A: Consider the three clusters A,B and C.Show in the following figure.Using Euclidean distance as the…

Q: Assume in K-Means algorithm, we assign three centers c1, c2 and c3 for three clusters. We usually…

A: K-Means Clustering is an unsupervised learning algorithm that is used to solve the clustering…

Q: Find several situations when the K-means clustering is not unique and the result (for the same…

A: K-means clustering K means that a data space is clustered in k clusters, with each a mean value.…

Q: Question 1 In the previous example, the K-Means algorithm was able to accurately find the three…

A: Task : Perform K-means clustering on the toy dataset. the clustering is performed using sklearn…

Q: You are given a cluster an of length n. You are approached to deal with q inquiries of the…

A: Here have to determine about the cluster an of length problem statement.

Q: Suppose that the data mining task is to cluster points into two clusters, where the points are 2, 3,…

Q: Consider the points RA (33,111) RB (33,66) RC (99,55) RD (66,99) RE (88,66) and centers as RA, RE.…

A: Given points : RA(33,111), RB(33,66), RC(99,55), RD(66,99), RE(88,66) Centers : RA, RE See the last…

Q: Suppose we cluster a set of N data points using two different using the k-means clusteringalgorithm…

A: Answer : The clusters created using different runs wont be the same , as we are trying to minimize…

Q: Suppose that a data set has been partitioned into two clusters, C1 and C2, with centroids c1 = (4,…

A: We need to find the within-cluster variation of given partitioning.

Q: might have been a simple undertaking, yet it worked out that you ought to observe a few guidelines:…

A: Here have to determine about the Array Destruction programming problem statement.

Q: Assume K-means clustering has been used to cluster two-dimensional data. There are 3 clusters i.e. K…

A: Introduction about K-means K-means clustering is the easiest algorithm for unlabeled data with type…

Q: Consider the points RA (33,111) RB (33,66) RC (99,55) RD (66,99) RE (88,66) and centers as RA, RE.…

A: The given question are multiple choice selected question so we can provide detailed explanation…

Q: onsider a set of data points {(-10,13), (-6,9), (-6,17), (-2,13), (-3,0)}. Use the k-means…

Q: Below is a dendrogram that describes a complete agglomerative clustering. b c de f g a How many…

A: Option a, b are correct whereas c, d are incorrect. If we see the dendrogram - There are 7…

Q: Consider performing the hierarchical agglomerative clustering algorithm on the following set of data…

A: Below i have answered:

Q: Consider the points RA (33,111) RB (33,66) RC (99,55) RD (66,99) RE (88,66) and centers as RA, RE.…

Q: Consider Self Organization Feature Map SOFM with two cluster units and five input Units. The weight…

A: Given: The weight vectors for the cluster units are:-W1 = (0.2, 0.6, 0.8, 0.2, 1.0) and W2 = (0.1,…

Q: Given an unlabeled dataset {x(1), ... , x(m)}. The K-mean algorithm has been run with 40 different…

A: Solution :: K-means clustering is an unsupervised form of clustering in which the input data…

Q: Q1. Given the following data, plot dendogram using complete linkage (agglomerative clustering),…

A: Provided the dendogram using complete linkage (agglomerative clustering) with detailed step by step…

Q: 4. If a model represents a system that includes random elements, it is called a------ 5. Medium is…

A: 6. host node are any physical or virtual computer system please specify the subject of remaining…

Q: Perform an agglomerative clustering of the following six points: А: (0, 0) B: (10,10) С: (21,21) D:…

A: Agglomerative clustering is a form of hierarchical clustering algorithm. It is an unmonitored gadget…

Q: Which of the following problems occur due to linear probing? O Primary clustering

A: Primary Clustering occurs due to linear probing So option A is correct

Q: In hierarchical clustering, observations start in their own cluster then observations are…

A: SOLUTION

Q: Consider the following data set consisting of the scores of two variables on each of seven…

A: Best Value for K you can find using elbow method. First we try random value for K then we use elbow…

Q: How about we signify by c0, c1 and c2 the number of numbers from the cluster a that have leftovers…

A: Here have to determine about the numbers from the cluster programming problem statement.

Q: Apply the k-means algorithm on the following dataset (x, y): (-1,- 0.75), (-1, – 1), (-0.75, - ..0),…

A: Given that (x, y): (-1, -0.75),(-1,-1), (-0.75, 1.0), (0.25, 0.25), (0.25,0.25), (0.75, 1.0),…

Q: In this question, you will be asked to demonstrate your understanding of how two different…

A: 1. The user must initially define k (the number of clusters), which is one of Simple k-means' most…

Concept explainers

Disjoint set forest

The concept of the disjoint set forest was introduced by Bernard A. Galler and Michael J. Fischer in 1964. The disjoint set forest is a method of implementing disjoint sets quickly. In the disjoint-set forest, each tree member points only to its parent. The root of every tree includes its representative and its parent. The disjoint set forest is a special kind of forest that performs Union and Find operations in near-constant amortized time.

Question

Suppose we have three cluster centroids µl=[1, 2],
µ2=[-3, 0] and µ3=[4,2]. Furthermore, we have a training
example x(i)=[-1,2]. After a cluster assignment step, which cluster
will x(i) belong to - c(1), c(2) or c(3)?

Expert Solution

This question has been solved!

Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.

This is a popular solution!

SEE SOLUTION Check out a sample Q&A here

Step 1

VIEW

Step 2

VIEW

Trending now

This is a popular solution!

Step by step

Solved in 2 steps with 11 images

SEE SOLUTION Check out a sample Q&A here

Knowledge Booster

Learn more about

Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, computer-science and related others by exploring similar questions and additional content below.

Similar questions

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
Consider a set of data points {(7, 30, 27), (11, 26, 22), (11, 34, 26), (15, 30, 27), (14, 17, 22), (27, 32, 30)}. Use the k-means clustering procedure with k = 3 to group these data points. Start with the three initial cluster centers: (-6, 9, 5), (-2, 13, 10), (10, 15, 13). Show your working for 2 iterations. Use D = √(xi-yi)2 for distance calculation.
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.
Correct answer will be upvoted else downvoted. Computer science. You can play out the accompanying activity: pick some subarray (adjoining subsegment) of an and improve the components in it in any capacity you need. Yet, this activity can't be applied to the entire cluster. For instance, if a=[2,1,4,5,3] and we need to apply the activity to the subarray a[2,4] (the subarray containing all components from the 2-nd to the 4-th), then, at that point, after the activity, the exhibit can become a=[2,5,1,4,3] or, for instance, a=[2,1,5,4,3]. Your assignment is to ascertain the base number of tasks portrayed above to sort the stage an in climbing request. Input The primary line contains a solitary integer t (1≤t≤2000) — the number of experiments. The primary line of the experiment contains a solitary integer n (3≤n≤50) — the number of components in the change. The second line of the experiment contains n particular integers from 1 to n — the given change a. Output :For…
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.…
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.
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 obtained. Describe how we should use Ex(1) – µ01² to choose the best m clustering.
Given a set of three points, -2, 0, and 9, we want to use k- Means Clustering with k = 2 to cluster them into two clusters. If the initial cluster centers are C1 = -4.0 and C2 = 2.0, What will be the center of C1 after completing first iteration of k means clustering algorithm? Answer:
Construct an overlap graph for F= (TACGA, ACCC, GACA, CTAAAG). Find ashortest common superstring for this collection.
Correct answer will be upvoted else downvoted. Computer science. Officially, you should find a cluster b1,b2,… ,bn, to such an extent that the arrangements of components of exhibits an and b are equivalent (it is identical 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 negligible 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 primary line contains a solitary integer t (1≤t≤100) — the number of experiments. The primary line of each experiment contains a solitary integer n (1≤n≤100). The second line of each experiment contains n integers a1,a2,… ,an (0≤ai≤100). Output For each experiment print an exhibit b1,b2,… ,bn — the ideal reordering of a1,a2,… ,an, so the amount of MEX on its prefixes is amplified. If there exist different ideal answers you can view as any
Develop a K-Mean Clustering program (using python) and classify the following data into 3 clusters. Individual 1, 3, 4 are the centroids of the initialize partitions. The clustering result should be output (e.g. using print function). Students must develop their own K-Mean Clustering program (K-means built function in any library should not be used.) Individual Variable 1 Variable 2 1 1.0 1.0 2 1.5 2.0 3 3.0 4.0 4 5.0 7.0 3.5 5.0 6. 4.5 5.0 7 3.5 4.5
Suppose there are six cities in a state. The distance matrix between each pair of the cities is given below. What is the dendrogram generated by hierarchical clustering with single-linkage? A В D E F A 60 120 185 260 300 60 90 210 258 320 120 185 90 140 179 200 210 132 140 179 125 E 260 258 125 135 300 320 200 132 135 Based on the resulting dendrogram, if we want to create 4 clusters, they are Cluster Number Items 1