K-Nearest Neighbor (kNN) Algorithm (Cont.)

The purpose of this kNN algorithm is to classify a new object based on attributes and training samples. Given a query point, we find K number of objects or (training points) closest to the query point. The classification is using majority vote among the classification of the K objects. Any ties can be broken at random. kNN used neighborhood classification as the prediction value of the new query instance. Notice the K in the figure should be 4 instead of 3.

An Example of kNN Algorithm
We have data from the questionnaires survey and objective testing with two attributes (acid durability and strength) to classify whether a special paper tissue is good or not. Here are four training samples:
X1=Acid Durability (seconds) X2=Strength (kg/m2) Y=Classification
7 7 Bad
7 4 Bad
3 4 Good
1 4 Good

Now the factory produces a new paper tissue that passed laboratory test with X1=3 and X2=7 . Without another expensive survey, can we guess what the classification of this new tissue is? Fortunately, kNN algorithm can help you to predict this type of problem.




      Why was the computer tired when he got home?    
      Because he had a hard drive.