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b) Given the data in Table 1 where, and are the input features of each data point. Using k-means clustering with d4 and ds as the initial centroids, calculate the next centroids after one update. Based on the values of the new centroids calculated, should the k- means clustering process be terminated? Why or why not? Table 1: 10 data points with and as input features F1 Iz d₁ 0.21 0.89 d₂ 0.22 0.83 da 0.74 0.25 da 0.83 0.69 ds 0.74 0.63 de 0.35 0.28 d7 0.76 0.89 da 0.54 0.45 de 0.44 0.94 dio 0.34 0.87

b) Given the data in Table 1 where, and are the input features of each data point. Using k-means clustering with d4 and ds as the initial centroids, calculate the next centroids after one update. Based on the values of the new centroids calculated, should the k- means clustering process be terminated? Why or why not? Table 1: 10 data points with and as input features F1 Iz d₁ 0.21 0.89 d₂ 0.22 0.83 da 0.74 0.25 da 0.83 0.69 ds 0.74 0.63 de 0.35 0.28 d7 0.76 0.89 da 0.54 0.45 de 0.44 0.94 dio 0.34 0.87

Database Systems: Design, Implementation, & Management
Database Systems: Design, Implementation, & Management
12th Edition
ISBN:9781305627482
Author:Carlos Coronel, Steven Morris
Publisher:Carlos Coronel, Steven Morris
Chapter11: Database Performance Tuning And Query Optimization
Section: Chapter Questions
Problem 11P: Problems 1114 are based on the following query: SELECT P_CODE, SUM(LINE_UNITS) FROM LINE GROUP BY...
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b) Given the data in Table 1 where and are the input features of 1₂ each data point. Using k-means clustering with d4 and ds as the initial centroids, calculate the next centroids after one update. Based on the values of the new centroids calculated, should the k- means clustering process be terminated? Why or why not? Table 1: 10 data points with and 2 as input features In I₂ d₁ 0.21 0.89 d₂ 0.22 0.83 da 0.74 0.25 da 0.83 0.69 d 0.74 0.63 de 0.35 0.28 d7 0.76 0.89 de 0.54 0.45 de 0.44 do 0.34 0.94 0.87
Transcribed Image Text:b) Given the data in Table 1 where and are the input features of 1₂ each data point. Using k-means clustering with d4 and ds as the initial centroids, calculate the next centroids after one update. Based on the values of the new centroids calculated, should the k- means clustering process be terminated? Why or why not? Table 1: 10 data points with and 2 as input features In I₂ d₁ 0.21 0.89 d₂ 0.22 0.83 da 0.74 0.25 da 0.83 0.69 d 0.74 0.63 de 0.35 0.28 d7 0.76 0.89 de 0.54 0.45 de 0.44 do 0.34 0.94 0.87
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