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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Concept explainers

Hash Table

A hash table also called a hash map, is a data structure that maps key-value pairs. The hash table can be used in complex systems to provide fast access and compact storage. A hash table is a structure that stores data in an abstract data type kind of associative array. It keeps data in an array format, with a distinct index value for every data value. If the index of the required data is known, then the data can be accessed very easily.

Question

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?

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