This is a good question, covering a lot of ground.

Here are brief descriptions of each type of set.

1.   Rough set.   A set A is rough, provided the difference between the lower approximation of A (written $A_*$, also written $A^{''}$) and upper approximation of A (written $A^) is nonempty.    The set $A^ - A_*$ is called the boundary region of the approximation of the set A.   The boundary region is nonempty whenever the set A is roughly approximated.   This very useful view of any nonempty set was introduced by Zdzislaw Pawlak during the early 1980s.   See the attached figure for an example from

http://cdn.intechweb.org/pdfs/5939.pdf

2.  Near sets.   Unlike the notions of rough set, shadow set and fuzzy set, the notion of nearness is applied to a pair of sets.   With near sets, we always think in terms of a pair of sets that are in some sense close to each other.   For example, if there is an overlap between the  upper approximation $A^*$ of a set A, the set $A^*$ is considered near the rough set $A$.   In general, a pair of sets A and B are near sets, provided A and B have elements in common, i.e., the intersection of A and B is nonempty.    See the attached figure for an example of 3D near sets, i.e., all points in the spherical neighbourhood C of the point p are in the interior of the spherical neighbourhood U of the point p.   This is an example of what are known as strongly near sets.    A paper on strongly near sets will be posted on RG in the near future.   For more about near sets,  see

J.F. Peters, Applications of near sets, Notices of the Amer. Math. Soc. 59, 2012, no. 4, 536-542:

http://www.ams.org/notices/201204/rtx120400536p.pdf

and Near Sets web page:

http://en.wikipedia.org/wiki/Near_sets

3.  Shadow set.   For each fuzzy set (FS), a shadow set (SS) , any membership values between a lower bound $\lambda$ and upper bound $1-\lambda$ belong a completely uncertain, shadow set SS region of the fuzzy set FS.    Shadow sets were introduced by Witold Pedrycz in 1998.   See the attached figure for an example from

http://www.people.vcu.edu/~mmanic/papers/2012/WCCI12_LindaManic%20ST2FSs.pdf

4.  Fuzzy set.   Let A be a subset in a space X.   The set A is fuzzy, provided every element of A has a degree of membership in the interval [0,1].    The degree of membership of an element x in A is characterized by the membership function

\[

\mu_A: X \righarrow [0,1]. \ \mbox{(degree of membership function)}

\]

The attached image for a shadow set also illustrates a fuzzy set.    Fuzzy sets were introduced by Lotfi Zadeh in his seminal 1965.    For more about this, see

http://uni-obuda.hu/users/fuller.robert/nfs1.pdf

Thank you so much sir...

if you find any reference in this case please send me

Rough set and Fuzzy set are different types of uncertainty. Rough set is more flexible and occupy more data than fuzzy set. Also, optimal solution extracted from an optimization problem  rough data is better than fuzzy data.   

As the Professor James F. Peters said, the rough set is a conception that definite only one set, while near set expressing the relation between the two sets.