Chapter 9 Data Analysis CS267 By Anand Sivaramakrishnan.

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Chapter 9 Data Analysis CS267 By Anand Sivaramakrishnan
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Transcript of Chapter 9 Data Analysis CS267 By Anand Sivaramakrishnan.

Page 1: Chapter 9 Data Analysis CS267 By Anand Sivaramakrishnan.

Chapter 9

Data AnalysisCS267

By Anand Sivaramakrishnan

Page 2: Chapter 9 Data Analysis CS267 By Anand Sivaramakrishnan.

A Decision table is defined as follows

T = (U,A,C,D)

where U = universe

A = set of actions

C = condition attributes

D = decision attributes

C,D is subset of A

Page 3: Chapter 9 Data Analysis CS267 By Anand Sivaramakrishnan.

Core is a condition attribute value which is indispensable that means it is that value that has direct impact on the value of decision attribute.

Reduct is a decision rule which must satisfy following conditions1. The rule must be true or consistent.2. Predecessor of rule must be independent.

Page 4: Chapter 9 Data Analysis CS267 By Anand Sivaramakrishnan.

To simplify the decision table

Removal of unnecessary or superfluous

Follow these steps.

1.Removing redundancy.

2.Checking functional dependency/ Removing superfluous attributes.

3.Categorizing the decisions into decision classes

4.Find core values of all decision rules.

5.Find the value reducts of each decision rule.

Page 5: Chapter 9 Data Analysis CS267 By Anand Sivaramakrishnan.

Table 1

Page 6: Chapter 9 Data Analysis CS267 By Anand Sivaramakrishnan.
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After removing all the redundant decision rules we get table 2

Table 2

Page 8: Chapter 9 Data Analysis CS267 By Anand Sivaramakrishnan.

• After removing attribute ‘a’

Table 3

Page 9: Chapter 9 Data Analysis CS267 By Anand Sivaramakrishnan.

• Therefore attribute ‘a’ is indispensable.

Page 10: Chapter 9 Data Analysis CS267 By Anand Sivaramakrishnan.

Removing attribute ‘b’

Table 4

Page 11: Chapter 9 Data Analysis CS267 By Anand Sivaramakrishnan.

• This means that after removing attribute ‘b’ the table remains consistent.

• That means attribute ‘b’ is a superfluous attribute

• It is dispensable.

Page 12: Chapter 9 Data Analysis CS267 By Anand Sivaramakrishnan.

After removing attribute ‘c’ we get Table 5

Table 5

Page 13: Chapter 9 Data Analysis CS267 By Anand Sivaramakrishnan.

In table 5 the following 2 rules are in consistent

that means attribute ‘c’ is indispensable.

Page 14: Chapter 9 Data Analysis CS267 By Anand Sivaramakrishnan.

After removing attribute ‘d’ we get Table 6 Table 6

Page 15: Chapter 9 Data Analysis CS267 By Anand Sivaramakrishnan.

In table 6, the following rules make it inconsistent

therefore attribute ‘d’ is indispensable

Thus, (a,c,d) is the D-core and also the D-reduct of C.

Page 16: Chapter 9 Data Analysis CS267 By Anand Sivaramakrishnan.

Therefore, we remove only attribute ‘b’, after which we also remove redundancy that occurred because of the removal.

Table 7

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• As there are similar decisions for different conditions, we can group similar decisions into decision classes.

(e2,f4) denoted as I

(e1,f4) denoted as II

(e2,f3) denoted as III

(e2,f2) denoted as IV

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Table 8

Now we compute which attribute values are dispensable and which ones are indispensable with respect to each decision class.

Values ‘a’ and ‘d’ are indispensable due to the following set of rules.

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Core Values Value Reduct

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In the decision classes I and II sets of core values of each decision rule are also reducts

but the same does not apply for classes III and IV.

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Value Reducts Table 10

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References

• Chapter 9 Data Analysis

• Chapter 8 Slides – Gayatri and Bhargav

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Thank YouQ&A