This document discusses rule-based classification in data mining. Rule-based classification uses "if-then" rules to make predictions, where the antecedent is the "if" condition and the consequent is the "then" prediction. An example rule is provided. Rules are assessed based on their coverage, which is the fraction of records satisfying the antecedent, and accuracy, which is the fraction of those records where the consequent is correct. However, rules may not be mutually exclusive or exhaustive. To address this, rules can be ordered as a decision list or votes can be assigned, and a default class can be used for uncovered records.

1. Rule Based Algorithms

2. Introduction
 Rule-based classification in data mining is a
technique in which class decisions are taken based
on various “if...then… else” rules. Thus, we define it
as a classification type governed by a set of IF-
THEN rules. We write an IF-THEN rule as:
“IF condition THEN conclusion.”
 IF-THEN rule can split it into two parts:
 Rule Antecedent: This is the “if condition” part of
the rule. This part is present in the LHS(Left Hand
Side). The antecedent can have one or more
attributes as conditions, with logic AND operator.
 Rule Consequent: This is present in the rule's
RHS(Right Hand Side). The rule consequent
consists of the class prediction.

3.  Example:
 R1: IF tutor = codingNinja AND student =
yes
 THEN happyLearning = true

4. Assessment of Rule
 In rule-based classification in data mining, there are two factors based
on which we can access the rules. These are:
 Coverage of Rule: The fraction of the records which satisfy the
antecedent conditions of a particular rule is called the coverage of
that rule. We can calculate this by dividing the number of records
satisfying the rule(n1) by the total number of records(n).
Coverage(R) = n1/n
 Accuracy of a rule: The fraction of the records that satisfy the
antecedent conditions and meet the consequent values of a rule is
called the accuracy of that rule.
We can calculate this by dividing the number of records satisfying the
consequent values(n2) by the number of records satisfying the
rule(n1).
Accuracy(R) = n2/n1
 Generally, we convert them into percentages by multiplying them by
100. We do so to make it easy for the layman to understand these
terms and their values.

5. Properties of Rule-Based Classifiers
 There are two significant properties of rule-based classification in data mining. They are:
 Rules may not be mutually exclusive
 Rules may not be exhaustive
 Rules may not be mutually exclusive in nature
 Many different rules are generated for the dataset, so it is possible and likely that many of
them satisfy the same data record. This condition makes the rules not mutually exclusive.
 Since the rules are not mutually exclusive, we cannot decide on classes that cover different
parts of data on different rules. But this was our main objective. So, to solve this problem, we
have two ways:
 The first way is using an ordered set of rules. By ordering the rules, we set priority orders.
Thus, this ordered rule set is called a decision list. So the class with the highest priority rule is
taken as the final class.
 The second solution can be assigning votes for each class depending on their weights. So, in
this, the set of rules remains unordered.
 Rules may not be exhaustive in nature
 It is not a guarantee that the rule will cover all the data entries. Any of the rules may leave
some data entries. This case, on its occurrence, will make the rules non-exhaustive. So, we
have to solve this problem too. So, to solve this problem, we can make use of a default class.
Using a default class, we can assign all the data entries not covered by any rules to the
default class. Thus using the default class will solve the problem of non-exhaustivity.

16. God Bless You ….!