and similarly dmin is the minimum distance. Final weightage of class k is given by Wki Vie class k. Final feature vector is concatenation of all codewords. For example, the query is represented by: [1 0 0 1001 1]. Use Hamming distance as similarity metric. Table 1 : Record of Mr. Smith's trend of visiting gym Day Temperature Car Condition Health Visit Gym Office Pressure HIGH 1 HOT NOT OK NOT FIT NO 2 HIGH HOT NOT OK FIT NO 3 MEDIUM HOT NOT OK NOT FIT YES 4 LOW MILD NOT OK NOT FIT YES 5 LOW COOL OK NOT FIT YES 6 LOW COOL OK FIT NO 7 MEDIUM COOL OK FIT YES 8 HIGH MILD NOT OK NOT FIT NO 9 HIGH COOL OK NOT FIT YES 10 LOW MILD OK NOT FIT YES 11 HIGH MILD OK FIT YES 12 MEDIUM MILD NOT OK FIT YES 13 MEDIUM HOT OK NOT FIT YES 14 LOW MILD NOT OK FIT NO and similarly dmin is the minimum distance. Final weightage of class k is given by Wki Vie class k. Final feature vector is concatenation of all codewords. For example, the query is represented by: [1 0 0 1001 1]. Use Hamming distance as similarity metric. Table 1 : Record of Mr. Smith's trend of visiting gym Day Temperature Car Condition Health Visit Gym Office Pressure HIGH 1 HOT NOT OK NOT FIT NO 2 HIGH HOT NOT OK FIT NO 3 MEDIUM HOT NOT OK NOT FIT YES 4 LOW MILD NOT OK NOT FIT YES 5 LOW COOL OK NOT FIT YES 6 LOW COOL OK FIT NO 7 MEDIUM COOL OK FIT YES 8 HIGH MILD NOT OK NOT FIT NO 9 HIGH COOL OK NOT FIT YES 10 LOW MILD OK NOT FIT YES 11 HIGH MILD OK FIT YES 12 MEDIUM MILD NOT OK FIT YES 13 MEDIUM HOT OK NOT FIT YES 14 LOW MILD NOT OK FIT NO