In data mining what is a class label..? please give an example

Very short answer: class label is the discrete attribute whose value you want to predict based on the values of other attributes. (Do read the rest of the answer.)

The term class label is usually used in the contex of supervised machine learning, and in classification in particular, where one is given a set of examples of the form (attribute values, classLabel) and the goal is to learn a rule that computes the label from the attribute values. The class label always takes on a finite (as opposed to inifinite) number of different values.

For a concrete example, we might be given a set of adult people and we'd like to predict whether they're homeless or not. Suppose the attributes were highest educational level achieved and origin (examples are of the from (origin, educationalLevel; isHomeless):

(Manhattan, PhD; no)
(Brooklyn, Primary school; yes)
...

In this particular case, isHomeless is the class label. The goal is to learn a function that computes whether the person with a given attribute values is homeless or not. (More specifically, to learn a function that makes as little mistakes as possible under a certain quantification of the number of mistakes.)

The Wikipedia article Supervised learning gives a good description.

Regarding the other question: no, a tuple means the whole set of values of the attributes in a given row. For example, if you had a table Table person(id, name, surname) then a tuple representing the first row could be (0, 'Akhil', 'Mohan').

Basically a class label (in classification) can be compared to a response variable (in regression): a value we want to predict in terms of other (independent) variables.

Difference is that a class labels is usually a discrete/Categorcial variable (eg-Yes-No, 0-1, etc.), whereas a response variable is normally a continuous/real-number variable.

You can find more about Regression and Classification related to Response variables and Class lables at https://math.stackexchange.com/questions/141381/regression-vs-classification.

Take an example of email spam filter, it classifies that an email is a spam or not, for which we define 2 classes which are spam(class 1) and not spam(class 2). Both of these are class labels or you can say that, if an email have some certain attributes then it belongs to spam class or not spam class