For the same dataset in Problem 1, ignore the Y variable and simply consider the X variables. Record X1 X2 # R1 3 5 R2 1 4 R3 3 2 R4 2 2 R5 4 1 We are now interested in answering questions on clustering based on the data set above. A. Fill up the distance matrix below the stipulates the distance from each record to every other record. Use wither Euclidean or Manhattan distance as the measure depending on your convenience Note: please create a table using the menu provided in the answer box to help illustrate your answer. R1 R2 R3 R4 R5 R1 R2 R3 R4 R5 B. Construct 2 clusters based on the distance matrix the you created. Can you improve these clusters? How would you measure this improvement?

For the same dataset in Problem 1, ignore the Y variable and simply consider the X variables. Record X1 X2 # R1 3 5 R2 1 4 R3 3 2 R4 2 2 R5 4 1 We are now interested in answering questions on clustering based on the data set above. A. Fill up the distance matrix below the stipulates the distance from each record to every other record. Use wither Euclidean or Manhattan distance as the measure depending on your convenience Note: please create a table using the menu provided in the answer box to help illustrate your answer. R1 R2 R3 R4 R5 R1 R2 R3 R4 R5 B. Construct 2 clusters based on the distance matrix the you created. Can you improve these clusters? How would you measure this improvement?

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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