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Discussion 9.1 - Association Rules

Essay by   •  January 13, 2018  •  Coursework  •  528 Words (3 Pages)  •  1,464 Views

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Assignment – 9.1

Bharat Surana Rajender Kumar Surana

Sullivan University

CSC550X - Data Mining

December 1, 2017


  1. Satellite Radio Customers. An analyst at a subscription-based satellite radio company has been given a sample of data from their customer database, with the goal of finding groups of customers that are associated with one another. The data consist of company data, together with purchased demographic data that are mapped to the company data (see Figure 13.5). The analyst decides to apply association rules to learn more about the associations between customers. Comment on this approach.

[pic 1]

From the above table we see that, there is no association between the rows that be found, and due to this reason association rules might not be the best approach. Also, association rules determine the association based on the item variables such as the columns in a data set. As the association rule defines associations between the purchases data or company data of a customer (Shmueli, Bruce, & Patel, 2016). Due to this, it will not help in analyzing the association in items among the rows or customers in this case. For this data, Cluster analysis may be a better approach when compared to the Associate rule, Cluster analysis is an exploratory data analysis tool and it aims at sorting distinctive objects into groups, in a certain way that there is maximal degree of association between two objects if they belong to the same group and minimal if they are not in the same group.

  1. Online Statistics Courses. Consider the data in the file CourseTopics.xls, the first few rows of which are shown in Figure 13.6. These data are for purchases of online statistics courses at statistics.com. Each row represents the courses attended by a single customer.

The firm wishes to assess alternative sequencings and combinations of courses. Use association rules to analyze these data and interpret several of the resulting rules.

[pic 2]

XLMiner : Association Rules

Output Navigator

Inputs

List of Rules

Inputs

Data

# Transactions in Input Data

365

# Columns in Input Data

8

# Items in Input Data

8

# Association Rules

12

Minimum Support

10

Minimum Confidence

50.000000%

Date: 03-Dec-2017 17:38:29

Elapsed Times in Milliseconds

AssocRules Time

Report Time

Total

32

0

32

List of Rules

Rule: If all Antecedent items are purchased, then with Confidence percentage Consequent items will also be purchased.

Row ID

Confidence %

Antecedent (A)

Consequent (C)

Support for A

Support for C

Support for A & C

Lift Ratio

1

62.5

DataMining & Regression

Cat Data

16

76

10

3.001644737

2

64.70588235

Intro & DOE

SW

17

81

11

2.915758896

3

50

Cat Data & Regression

DataMining

20

65

10

2.807692308

4

55.55555556

DataMining & Cat Data

Regression

18

76

10

2.668128655

5

54.54545455

Intro & Survey

SW

22

81

12

2.457912458

6

53.84615385

Intro & Regression

SW

26

81

14

2.42640076

7

50

Intro & DataMining

Regression

20

76

10

2.401315789

8

70

Regression & SW

Intro

20

144

14

1.774305556

9

66.66666667

Survey & SW

Intro

18

144

12

1.689814815

10

62.5

DataMining & Regression

Intro

16

144

10

1.584201389

11

60

Cat Data & Regression

Intro

20

144

12

1.520833333

12

52.38095238

DOE & SW

Intro

21

144

11

1.32771164

...

...

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