Which one of the following statements best describes k-means clustering and its utility? K-means clustering reduces the amount of variables in the model to determine which features have the most variability. Press 1 K-means clustering groups data into relatively distinct groups by using a pre- determined number of clusters and iterating cluster assignments. Press 2 K-means clustering is a supervised learning algorithm that predicts continuous variables as a function of categorical variables (called clusters). Press 3 K-means clustering is a supervised learning algorithm that predicts categorical variables (called clusters) as a function of continuous variables. Press 4

Which one of the following statements best describes k-means clustering and its utility? K-means clustering reduces the amount of variables in the model to determine which features have the most variability. Press 1 K-means clustering groups data into relatively distinct groups by using a pre- determined number of clusters and iterating cluster assignments. Press 2 K-means clustering is a supervised learning algorithm that predicts continuous variables as a function of categorical variables (called clusters). Press 3 K-means clustering is a supervised learning algorithm that predicts categorical variables (called clusters) as a function of continuous variables. Press 4

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

Similar questions

Electronic Spreadsheet Applications Compare What-If Analysis using Trial and Error and Goal Seek to the given scenario: Let's say a student is enrolled in an online class at a learning institution for a semester. His overall average grade stands at 43% in the course (Term Grade is 45%, Midterm Grade is 65%, Class Participation is 62% and Final Exam is 0%). Unfortunately, he missed his Final Exam and was given 0%. However, he has the opportunity to redo his Final Exam and needs at least an overall average of 60% to pass the course. How can you use Trial and Error and Goal Seek to find out what is the lowest grade he needs on the Final Exam to pass the class? Which method worked best for you and why?
You are getting continuous feeds from stock market. You need to develop an algorithm for real-time clustering of stock patterns. Develop an algorithm and explain it. Can you use incremental learning here for cluster refinement? Which distance measure would you use for this clustering? Why? Explain overfitting and underfitting in above case. What are possible issues if you use Manhattan distance-based clustering in above case?
The procedure of evaluating the results of a clustering algorithm is known under the termcluster validity. In general terms, there are two approaches to investigate cluster validity Internal and External criteria. Both DB (Davies-Bouldin) and Silhouette Coefficient are internal criteria. Which one is NOT correct about these two criteria? Group of answer choices The minimum DB score is zero, with lower values indicating better clustering. DB is a measure of how similar an object is to its own cluster (cohesion) compared to other clusters (separation). The best value of Silhouette is 1 and the worst value is -1 No answer text provided.
Correct answer will be upvoted else Multiple Downvoted. Don't submit random answer. Computer science. You are given an exhibit [a1,a2,… ,an]. You will probably track down the length of the longest subarray of this cluster with the end goal that the most continuous worth in it isn't interesting. At the end of the day, you are searching for a subarray with the end goal that assuming the most successive worth happens f times in this subarray, something like 2 distinct qualities ought to happen precisely f times. An exhibit c is a subarray of a cluster d if c can be gotten from d by erasure of a few (potentially, zero or all) components from the start and a few (conceivably, zero or all) components from the end. Input The primary line contains a solitary integer n (1≤n≤200000) — the length of the exhibit. The subsequent line contains n integers a1,a2,… ,an (1≤ai≤min(n,100)) — components of the cluster. Output You should output precisely one integer — the length of the…
Subject: Machine Learning Consider the following data set consisting of the scores of two variables on each of seven individuals. This data set is to be grouped into two clusters. Let the individual 1 & 4 be the initial cluster centers i.e., the two centers are (1.0, 1.0) and (5.0, 7.0). Now, apply the K-Means clustering techniques to find new centers with associated cluster members.
Which of the following statements is true? Group of answer choices When clustering, we want to put two dissimilar data objects into the same cluster Clustering is among unsupervised learning models since it does not require a target variable Clustering is among unsupervised learning models since it requires a target variable When clustering, we want to put data objects into a pre-labeled target variable.
You are given a dataset of continuous numerical features with a categorical target class. There is not any missing data or outliers. Which algorithm would you use to create a model? K-means clustering Logistic regression Decision Tree Linear regression
In Hierarchical clustering, the major difference between the Ward's method and other linkage method is that a. Ward's method uses error sum of squares to measure the loss of information that occurs when cluster are created. b. Ward's method minimizes MAD to minimize the dissimilarity between clusters c. Ward's method uses minimum distance while other linkage methods uses maximum distance between the data points Clear my choice
Examine weighted graphs and adjacency lists' pros and cons.
int foo( Graph G(V,E)):: int d[V]; } for(VvE V) {Q.push(v); } for(V(x, z) E E){ print hello; } while( Q not empty) { a=Q.pop(); for(all x adj to a) //x--->a print x points to a; } for(int i= 0 to V-1) { for( u € Adj[i]){ print (i,u); } d[i]=BFS(G(V,E),i); }//endfor
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.
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.