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

2. 2 Outline What is Data Analysis? Why data should be analyzed? What is a Decision Table? Condition/Decision attributes of the Stoker table Analyzing Inconsistent data in the Stoker table Analyzing Consistent data in the Stoker table Conclusion

3. Data Analysis  Data Analysis is a process of gathering, modelling and transforming data with a goal of highlighting useful information, suggesting conclusions, and supporting decision making.  Data Analysis is the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data. Ways to analyze data  Compare constantly  Categorize and sort

4. Why data should be analyzed?  Data Mining is a particular data analysis technique that focuses on modeling and knowledge discovery for predictive rather than purely descriptive purposes.  Data analysis does NOT explain but it does summarize and provide organization to data.  It tries to make sense of these rows and columns in the decision tables.

5. 5 Decision Table  According to Wiki enclycopedia a decision table are a precise yet compact way to model complicated logic.  Decision tables, like if-then-else and switch-case statements, associate conditions with actions to perform.  The structure of Decision tables are typically divided into four quadrants, as shown below: ConditionsCondition alternatives Actions Action entries The four quadrants  Each decision corresponds to a variable, relation or predicate whose possible values are listed among the condition alternatives.  Each action is a procedure or operation to perform, and the entries specify whether (or in what order) the action is to be performed for the set of condition alternatives the entry corresponds to.  In this presentation we will use the Stoker decision table as the example of a decision table.

6. 6 Decision Table as Protocol of Observations We will analyze the stoker’s decisions while controlling the clinker kiln Aim of the stoker is to keep the kiln in a “proper” state Actions of the stoker can be described by a “Decision Table”

7. 7 Actions of the stoker can be described by a decision table, where 1. Burning zone temperature(BZT) 2. Burning zone color(BZC) Condition 3. Clinker granulation(CG) attributes 4. Kiln inside color(KIC) 1. Kiln revolutions(KR) Decision 2. Coal worm revolutions(CWR) attributes Condition/Decision attributes of the Stoker table

8. 8 The table describes action undertaken by a stoker, during one shift, when specific conditions observed by the stoker have been fulfilled numbers given in the column TIME are in fact ID labels of moments of time when the decision took place, and form the “universe” of the decision table. Since many decisions are the same, hence identical decision rules can be removed It can be described by table shown below,

9. 9 Decision Table 1 with Condition Attributes and Decision Attributes

10. 10 Derivation of Control Algorithms from Observation Consistency of the stoker’s knowledge The reduction of his knowledge and control algorithm generation from the observed stoker’s behavior

11. 11 Table 2: Removing attribute ‘a’ from the table1 Ubcdef 132224 222224 322124 422114 522214 622323 732323 832323 933322 1043322 1143222 1233222 1323222

12. 12 Table 4: Removing attribute ‘c’ from table1 Uabdef 133224 232224 332124 422114 522214 632323 733323 843323 943322 1044322 1144222 1243222 1342222

13. Table 5: Removing attribute ‘d’ from table1 Uabcef 133224 232224 332224 422214 522214 632223 733223 843223 943322 1044322 1144322 1243322 1342322

14. 14 Table 3: Removing attribute ‘b’ from table1 Uacdef 132224 232224 332124 422114 522214 632323 732323 842323 943322 1043322 1143222 1243222 1343222

15. 15 Table 6: After removing superfluous attribute ‘b’ & duplicate rules u acdef 132224 232124 322114 422214 532323 642323 743322 843222

16. 16 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

17. 17 Table 6: Inconsitent rows without column a ucdef 12224 22124 32114 42214 52323 62323 73322 83222

18. 18 u adef 13224 23124 32114 42214 53323 64323 74322 84222 Table 6: Inconsitent rows without column c

19. 19 Table 6: Inconsitent rows without column d u acef 13224 23224 32214 42214 53223 64223 74322 84322

20. 20 Conclusion  We determined that condition attributes a, c, and d were core attributes to the Stoker table because when they were removed respectively from the Stoker table, the Stoker table contained inconsistent rows;  We then determined that condition attribute b was a reduct or superfluous attribute of the Stoker table, because when it was removed from the Stoker table the table still contained consistent rows.  From there we were able to build our final Stoker decision table and algorithms.

21. 21 Thank you