The Inductive Learning Algorithm in Machine Learning is as follows -

  1. Divide the dataset table "T" containing m examples into n sub-tables (t1, t2, t3.....n). One table for each possible value of the class attribute (repeat steps 2-8 for each sub-table).
  2. Initialize the attribute combination count j = 1.
  3. For the sub-table for which the work is going on, divide the attribute list into distinct combinations, each combination with j distinct attributes.
  4. For each combination of attributes, count the number of occurrences of attribute values that appear under the same combination of attributes in unmarked rows of the sub-table under consideration, and at the same time, not appear under the same combination of attributes of other sub-tables. Call the first combination with the maximum number of occurrences the max-combination MAX.
  5. If MAX == null, increase j by 1 and goto Step 3.
  6. Mark all rows of the sub-table where working, in which the values of MAX appear, as classified.
  7. Add a rule (If attribute = "XYZ" then the decision is YES/NO) to R (ruleset) whose left-hand side will have attribute names of the MAX with their values separated by AND, and its right-hand side contains the decision attribute value associated with the sub-table.
  8. If all rows are marked as classified, then move onto process another sub-table and go to Step 2, else, go to Step 4. If no sub-tables are available, exit with the set of rules obtained till then.