Data Mining

Chapter 9 - Data Analysis Presented by, Professor, Hair Priya Pucchakayala Dr. T. Y. Lin & Donavon Norwood

Outline ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

[object Object],[object Object],[object Object],[object Object]

Decision Table ,[object Object],[object Object],[object Object],The four quadrants ,[object Object],[object Object],[object Object],Conditions Condition alternatives Actions Action entries

Decision Table as Protocol of Observations ,[object Object],[object Object],[object Object],[object Object]

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Condition/Decision attributes of the Stoker table

[object Object],[object Object],[object Object]

Decision Table 1 with Condition Attributes and Decision Attributes

Derivation of Control Algorithms from Observation ,[object Object],[object Object]

Table 2: Removing attribute ‘a’ from the table1 U b c d e f 1 3 2 2 2 4 2 2 2 2 2 4 3 2 2 1 2 4 4 2 2 1 1 4 5 2 2 2 1 4 6 2 2 3 2 3 7 3 2 3 2 3 8 3 2 3 2 3 9 3 3 3 2 2 10 4 3 3 2 2 11 4 3 2 2 2 12 3 3 2 2 2 13 2 3 2 2 2

Table 4: Removing attribute ‘c’ from table1 U a b d e f 1 3 3 2 2 4 2 3 2 2 2 4 3 3 2 1 2 4 4 2 2 1 1 4 5 2 2 2 1 4 6 3 2 3 2 3 7 3 3 3 2 3 8 4 3 3 2 3 9 4 3 3 2 2 10 4 4 3 2 2 11 4 4 2 2 2 12 4 3 2 2 2 13 4 2 2 2 2

Table 5: Removing attribute ‘d’ from table1 U a b c e f 1 3 3 2 2 4 2 3 2 2 2 4 3 3 2 2 2 4 4 2 2 2 1 4 5 2 2 2 1 4 6 3 2 2 2 3 7 3 3 2 2 3 8 4 3 2 2 3 9 4 3 3 2 2 10 4 4 3 2 2 11 4 4 3 2 2 12 4 3 3 2 2 13 4 2 3 2 2

Table 3: Removing attribute ‘b’ from table1 U a c d e f 1 3 2 2 2 4 2 3 2 2 2 4 3 3 2 1 2 4 4 2 2 1 1 4 5 2 2 2 1 4 6 3 2 3 2 3 7 3 2 3 2 3 8 4 2 3 2 3 9 4 3 3 2 2 10 4 3 3 2 2 11 4 3 2 2 2 12 4 3 2 2 2 13 4 3 2 2 2

Table 6: After removing superfluous attribute ‘b’ & duplicate rules u a c d e f 1 3 2 2 2 4 2 3 2 1 2 4 3 2 2 1 1 4 4 2 2 2 1 4 5 3 2 3 2 3 6 4 2 3 2 3 7 4 3 3 2 2 8 4 3 2 2 2

a3c2d2 ==> e2f4 c2d2 ==> e2f4 (rule 1) c2d2 ==> e1f4 (rule 4) a3c2 ==> e2f4 (rule 1) a3c2 ==> e1f3 (rule 5) For e.g . let us compute core values and reduct values for the first decision rule: In the table 7, values ‘a’ and ‘d’ are indispensible in the rule Since the following pairs of rules are Inconsistent. Thus a3 and d2 are core values of the decision value a3c2d2 ---> e2f4

Table 6: Inconsitent rows without column a u c d e f 1 2 2 2 4 2 2 1 2 4 3 2 1 1 4 4 2 2 1 4 5 2 3 2 3 6 2 3 2 3 7 3 3 2 2 8 3 2 2 2

Table 6: Inconsitent rows without column c u a d e f 1 3 2 2 4 2 3 1 2 4 3 2 1 1 4 4 2 2 1 4 5 3 3 2 3 6 4 3 2 3 7 4 3 2 2 8 4 2 2 2

Table 6: Inconsitent rows without column d u a c e f 1 3 2 2 4 2 3 2 2 4 3 2 2 1 4 4 2 2 1 4 5 3 2 2 3 6 4 2 2 3 7 4 3 2 2 8 4 3 2 2

Conclusion ,[object Object],[object Object],[object Object]

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