hiii
could you plez send to me the source code for the rough set
thank you in advance

Introduction
Rough Set theory, proposed by Pawlak, is an effective approach for data analysis and its main goal is synthesizing approximation of a crisp set in terms of a pair of sets which give the upper and lower approximation of the original set. The starting point of Rough Set theory is an observation that the objects having the same description (same attribute-values) are indiscernible (similar) with respect to the set of defined attributes and this process is very hard and sensible when some attribute-values are missing. With Rough Set Sara tool, I tried to demonstrate some of Rough set concepts for non-expert users. Also, you can update this tool to define other concepts.

Background
To introduce the concepts of Rough Sets, let's begin with a data table IS whose rows represent a set of objects in the universe (e.g. persons, balls, books, etc.) and columns represent a set of attributes that describe those objects (e.g. color, shape, age, etc.), formally IS= (U, AT), an Information table "attribute-value System", where U is the set of objects and AT is the set of attributes. Every attribute a ∈ AT has a set of values Va called "Domain of a" such that a: U→ Va. The information table assigns a value a(x) from Va to each attribute a and each object x in the universe U. If Va contains missing value for at least one attribute a, then IS is called incomplete information table, otherwise it is complete.

With any subset of attributes P AT, there is an associated equivalence relation IND (P) = {(x, y) ∈ U2 | ∀ a ∈ P, a(x) =a(y)}. The relation IND (P) is called a P-indiscernibility relation.

IND (P) = ∩ IND (a) where a ∈ P.

A fundamental problem discussed in rough set is whether the whole knowledge extracted from data sets is always necessary to classify objects in the universe; this problem arises in many practical applications and will be referred to as knowledge reduction. The two fundamental concepts used in knowledge reduction are the core and reduct. Intuitively, a reduct of knowledge is its essential part, which suffices to define all basic classifications occurring in the considered knowledge, whereas the core is in a certain sense it’s most important part. Let P set of attributes and let a ∈ P, the attribute a is dispensable in P if: IND (P) =IND (P - {a}), otherwise a is indispensable attribute. The set of attributes A, where A P is called a reduct of P if: IND (A) = IND (P) and P may have many reducts denoted as RED (P). The set of all indispensable attributes in P will be called the core of P and denoted as CORE (P) where CORE (P) = ∩ RED (P).

When the full set of attributes is considered P= {a, b, c, d}, we see the following equivalence classes "classification of the objects in the universe":

IND (P) = {{1}, {2}, {3}, {4}, {5}}

Thus all objects are distinguishable under all set of attributes but if we exclude the attribute b from the set of attributes, we see the following equivalence classes:

IND (P - {b}) = {{1}, {2, 5}, {3}, {4}}

Thus objects 2&5 are classified as being similar under this set of attributes which means that the attribute b is important in distinguishing those two objects from each other.

IND (P - {b}) ≠ IND (P) which means that the two classifications are not equal. So, b is indispensable attribute.

While IND (P - {a}) = {{1}, {2}, {3}, {4}, {5}} and thus IND (P - {a}) = IND (P). So, a is dispensable attribute and the set= {b, c, d} without attribute a represent one reduct of the set P.

The reduct set can be used to represent the same classification as a set of attribute P but with minimum number of attributes required for this classification. The intersection among all reducts of the set P produces the core set of attributes which represent the most important ones in performing classification correctly.

Using the code
First, the information table that contains a set of attribute-values is located in the text file named Table in Debug folder. You can change these values according to your needs and your information but note in the same format as provided in the text file.

For simplicity I gave the attributes names such as a, b, c, etc. and these names are given automatically according to the number of columns you inserted in the Table file. Also, the number of objects is computed according to the number of rows in the text file.

My code has three classes: Attribute, Object, and RoughSet. The class Attribute defines an attribute name and value for each object in the universe. The class Object defines an object in the universe with its attribute-values information. The class RoughSet defines a framework for the information table that is supplied in the text file (Table) with a set of rough set operators that are discussed previously such as:

Indecernibility () ---->IND (AT) where AT= all attributes defined in the information table that are given in the text file Table.
Indecernibility (object NameAttr) ---->IND (AT - {a}) where the indecernibility is computed here with excluding the attribute a that is passed to the method by its name. Moreover, you can update this method by providing a list of excluded attributes or other defined attributes such as b, d, ..etc.
Also, there are a set of methods to compute dispensable, indispensable attributes as well as core and reducts.

Collapse | Copy Code
// Compute Indecernibility under all defined attributes AT in the information table "Table"
public List<List<object>> Indecernibility()
{
List<List<object>> Result = new List<List<object>>();
for (int i = 0;i< __Objects.Count; i++)
{
List<object> Temp = new List<object>();
Temp.Add(__Objects[i].__Name);

for (int j = 0; j < __Objects.Count; j++)
{
if (i == j)
continue;
else
{
List<Attribute> AttVali = ((List<Attribute>)h[__Objects[i].__Name]);
List<Attribute> AttValj = ((List<Attribute>)h[__Objects[j].__Name]);
for (int f = 0; f < AttVali.Count; f++)
{
if (AttVali[f].value.ToString() == AttValj[f].value.ToString())
{
if (f == AttVali.Count - 1)
{
Temp.Add(__Objects[j].__Name);
}//Reach the Last Attribute
else
{
continue;
}
}// If Attributes_Values are Equal
else
{
break;
}// If Attributes_Values are Not Equal
}//Loop to Search All Atributes
}//Else To Not Compare each item with itself
}//For Jitems

Result.Add(Temp);
}// For Iitems
return Result;
}
References
For more information about Rough Sets and its Concepts, read the following book "Rough Sets: Theoretical Aspects of Reasoning about data" by Zdzislaw Pawlak.