It Store the training records and use training records to predict the class label of unseen cases.

Examples:

## i.  Rote-learner

• Memorizes entire training data and performs classification only if attributes of record match one of the training examples exactly

## ii. Nearest neighbor

• Uses k “closest” points (nearest neighbors) for performing K-closet neighbor of a record ‘X’ are data points that have the K-smallest distance of ‘X’.
• Classification based on learning by analogy e. by comparing a given test tuple with training tuple that are similar to it.
• Training tuples are described by n-attributes.
• When given an unknown tuple, a k-nearest- neighbor classifier searches the pattern space for the k-training tuples that are closest to the unknown tuple.
• Nearest neighbor classifier requires:
• Set of stored records
• Distance matric to compute distance between For distance calculation any standard approach can be used sch as Euclidean distance.
• The value of ‘K’, the number of nearest neighbor to retrieve.
• To classify the unknown records
• Compute distance to other training records
• Identify the k-nearest neighbor.
• Use class label nearest neighbors to determine the class label of unknown record.                In case of conflict, use majority vote for classification.

## Issues of classification using k-nearest neighbor classification

1. Choosing the value of K
• One of challenge in classification is to choose the appropriate value of K. If K is too small, it is sensitive to noise points. If K is too large, neighbor may include points from other classes.
• With the change of value of K, the classification result may vary. ## ii.  Scaling Issue

• Attribute may have to be scaled to prevent distance measure from being dominated by one of attributes. Eg. Height, Temperature etc.

## iii.  Distance computing for non-numeric data.

• Use Distance as 0 for the same data and maximum possible distance for different data.

## iv.  Missing values

• Use maximum possible distance