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Overview

In this chapter proposed method is explained step by step where different parameters are covered for hiding the direct discriminating items. Whole work is broadly divided into few blocks such as pre-processing, frequent rule generation from the dataset which help in identifying the information, next is to filter those rules from the frequent set that discriminate attribute values from others. Finally all filtered rules are perturbed one by one so that all the discriminate attribute values will not lead to any strong decision which is unfavorable.

Basic Notions

The data set is a combination of object of data and their attribute. An item is an attribute along with its value, e.g. {Race=black}. An item set, i.e. X, is a collection of one or more items, e.g. {Foreign worker=Yes, City=NYC}. A classification law is an expression Xà C, where C is a class item (a yes/no decision), and X is an item set containing no class item, e.g. {Foreign worker=Yes, City=NYC} --> {hire=no}. X is said the premise of the law.

Separate Direct and Indirect Rules

Now from the generated rule one can get bunch of rules then it is required to separate those rules from the collection into direct and indirect rule set. Those rules which contain dicriminant items are identified as the direct rules while those not containing are indirect rules. This can be understood as the Let A, B àC where A is set of discriminent  item then this rule is direct rule, where B, C are non discriminent items. If D, Bà C is a rule and D is the non discriminate item set the this rule is not direct rule.

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