Chapter 4

Question 1

Entity

Answer 1

Something the business has the will and means to keep information about

Question 2

Attribute

Answer 2

A single piece of information about an entity

Question 3

Relationship

Answer 3

An association of two entities for a business purpose

Question 4

Unique identifier

Answer 4

Attribute(s) that uniquely identifies an instance of an entity

Question 5

relationship cardinality

Answer 5

Describes how many instances of an entity can be related to instances of other entity in the relationship

Question 6

ERD

Answer 6

Entity-relationship diagram

Question 7

Master data

Answer 7

the official list of data

Question 8

MDM

Answer 8

master data management

Question 9

SQL

Answer 9

Structured query language

Question 10

data mining*

Answer 10

The nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns of data stored in data bases

Question 11

data mining key words

Answer 11

process
nontrivial
valid
novel
potentially useful
understandable

Question 12

data mining is a blend of multiple disciplines

Answer 12

statistics
AI
machine learning
information visualization

Question 13

types of patterns

Answer 13

association
prediction
cluster
sequence

Question 14

Prediction

Answer 14

classification
regression
time series

Question 15

association

Answer 15

market based
link analysis
sequence analysis

Question 16

segmentation

Answer 16

clustering

outlier analysis

Question 17

CISPDM

Answer 17

Cross industry standard process for data mining

Question 18

CISPDM steps

Answer 18

business understanding
data understanding
data preparation
model building
testing and evaluation
deployment

Question 19

Association rule input

Answer 19

the simple point of transaction

Question 20

Association rule output

Answer 20

most frequent affinities among items

Question 21

Data mining mistakes

Answer 21

• Selecting the wrong problem for data mining
• Ignoring what your sponsor thinks data mining is and what it really can/cannot do
• Beginning without an end in mind
• Not leaving sufficient time for data acquisition, selection, and preparation
• Looking only at aggregated results and not at individual records/predictions