BCS 3543 DATA MINING Data Mining Fuzzy System

BCS 3543 DATA MINING

Data Mining : Fuzzy System Chapter 10

Topics Knowledge Discovery in Databases (KDD) Process • Introduction • What is Fuzzy Logic ? • Fuzzy Logic Analysis Method • Fuzzy Logic System • Application of Fuzzy System

Introduction � Fuzzy concepts first introduced by Zadeh in the 1960 s and 70 s � Traditional computational logic and set theory is all about true or false zero or one in or out (in terms of set membership) black or white (no grey) � Not the case with fuzzy logic and fuzzy sets! � Fuzzy logic attempts to reflect the human way of thinking � As a result, it is leading to new, more human, intelligent systems. � Ref : https: //www. youtube. com/watch? v=rln_k. Zb. Ya. Wc

What is Fuzzy Logic ? � Fuzzy logic is a set of mathematical principles for knowledge representation based on degrees of membership. � Unlike two-valued Boolean logic, fuzzy logic is multi-valued. � It deals with degrees of membership and degrees of truth. � Fuzzy logic uses the continuum of logical values between 0 (completely false) and 1 (completely true).

FL Control Analysis Method

Fuzzy Logic System � Is a control system based on fuzzy logic. � Usually fuzzy logic system is created elements presented below : 7 from five major

Fuzzy System Operation Fuzzification: � Process of transform crisp value into grades of membership for linguistic terms. Fuzzy rule based: � Collection of propositions containing linguistic variables. Fuzzy inferencing: � Combine the facts obtain from fuzzification with the rule based n conduct the fuzzy reasoning process. Membership function: � Provides measure of similarity elements. Defuzzification: � Translate result back to the real world value. 8

Application : Air Conditioner Let us consider an air conditioning system with 5 -level fuzzy logic system. � This system adjusts the temperature of air conditioner by comparing the room temperature and the target temperature value. � 9

Algorithm Step 1 : Define linguistic variables and terms. Initialize Step 2 : Construct membership functions for them. Membership Function 3. Step 3 : Construct knowledge base of rules. Fuzzy Rule Based 4. Step 4 : Obtain Fuzzy Value ▪ Convert crisp data into fuzzy data sets using membership functions. Fuzzification‘ ▪ Evaluate rules in the rule base. Combine results from each rule. Fuzzy inferencing 5. Step 5 : Convert output data into non-fuzzy values. Defuzzification 1. 2.

Logic Development Step 1: Define linguistic variables and terms Linguistic variables are input and output variables in the form of simple words or sentences. Ø For room temperature, cold, warm, hot, etc. , are linguistic terms. Ø Temperature t = {very-cold, warm, very-warm, hot} Ø

Logic Development � Step 2: Construct membership functions for them Ø The membership functions of temperature variable are as shown

Logic Development Step 3: Construct knowledge base rules Ø Create a matrix of room temperature values versus target temperature values that an air conditioning system is expected to provide.

Logic Development � Build a set of rules into the knowledge base in the form of IF- THEN-ELSE structures.

Logic Development � Step 4: Obtain fuzzy value Ø Fuzzy set operations perform evaluation of rules. The operations used for OR and AND are Max and Min respectively. Combine all results of evaluation to form a final result. This result is a fuzzy value.

Logic Development � Step 5: Perform Defuzzification Ø Defuzzification is then performed according to membership function for output variable.

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