hi, i think can help you. you download book (++2011 Data_Mining__Concepts_and_Techniques__3rd_Edition of jiawei han) for more help, you see page 402/740 this book.
True positives (TP): These refer to the positive tuples that were correctly labeled by the classifier. Let TP be the number of true positives.
True negatives (TN): These are the negative tuples that were correctly labeled by the classifier. Let TN be the number of true negatives.
False positives (FP): These are the negative tuples that were incorrectly labeled as positive (e.g., tuples of class buys computer = no for which the classifier predicted buys computer= yes). Let FP be the number of false positives.
False negatives (FN): These are the positive tuples that were mislabeled as negative (e.g., tuples of class buys computer = yes for which the classifier predicted buys computer= no). Let FN be the number of false negatives.
As others already said, this concepts (TP, FP,...) are only applicable to binary classification problems. Consequently, there is not such a definition for a multi class problem. However, you may use an one-against-all evaluation which may compute the TP/TN/...etc. relatively to one class (i.e.C1 as suggested by Ekarin).