Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method
Used on-line to identify users click stream data, matching it to a particular user group and recommend a tailored browsing option.
- K-Nearest Neighbor:
learning by analogy → learnt by comparing a test tuple with a set of similar (close, wrt a distance) training tuples ⇒ classified based on the class of their $k$-closest neighbors
Lazy learner: stores the given training tuples and waits until given a test tuple, then performs generalization
instance based learner: does less work when presented with training tuples and more work during classification and prediction ⟹ computational expensive (unlike eager learners, constructing classification model before receiving the test tuple)
Distance used: Euclidian distance