A = PDP-1
First of all, PCA is neither used for classification, nor clustering. It is an analysis tool for data where you find the principal components in the data. This can be used for e.g. dimensionality reduction. Supervised and unsupervised learning has no relevance here.
However, PCA can often be applied to data before a learning algorithm is used.
In supervised learning, you have (as you say) a labeled set of data with "errors".
In unsupervised learning you don't have any labels, i.e, you can't validate anything at all. All you can do is to cluster the data somehow. The goal is often to achieve clusters that internally are more homogeneous. Success can be measured, e.g., using the within-cluster variance metric.
-> You give variously labeled example data as input along with correct answer.
-> This algorithm will learn form it and start predicting correct result based on input.
email spam filter-> You gave just data and don't tell anything like label or correct answer.
-> Algorithm automatically analyse pattern in the data.
google news
