Question 1 (25%) A. Answer the following questions with a True or False and briefly justify your answers. (12 Marks) 1) In a broad sense, a knowledge-based system consists of a database and a knowledge base 2) To convert numeric attributes to categorical ones, the normalization process should be applied to the numeric attributes in some situation 3) A decision tree can be transformed into a linear regression model that can be used for the same purpose 4) Given a set of production rules, the relation between the rules is disjunctive 5) Neural network classifiers are normally considered to be easy to interpret 6) A continuous attribute is a numeric attribute in a data mining/machine learning system 7) Disjoint training and test datasets are required to estimate the performance of classifiers 8) K-means algorithms can automatically classify new instances into pre-defined categories 9) A common weakness of association rule mining is that it produces too many rules 10) Data sets represented by a matrix of instances versus attributes are suitable for machine learning and data mining applications 11) The use of majority values of attributes to replace missing values is a common practice in machine leaming tasks 12) 10-folder cross validation is not an effective method for evaluating machine learning algorithms B. Briefly describe three strategies for conflict resolution in production rule systems, and use three respective examples to show how these strategies can be used to resolve the conflict 6 Marks) between production rules. C. What is the major difficulty when converting a set of production rules to a tree, use two rules (7 Marks) below to illustrate the difficulty IF a and b THEN x IF c and d THENX