knn's Introduction

KNN

To know more about this project, please refer to https://dl.dropboxusercontent.com/u/37572555/Github/KNN/KNN.pdf

This is a course assignment, and we are supposed to implement the basic K-Nearest Neighbor algorithm. Below are the basic classes deisgned in the initial phase:

FileManager
in charge of file operations
- ReadFile: read training files and test files
- ProcessData: normalize and standaize the data
- WriteFile: output the predicted labels
Metric
interface for defining different measurement methods
- CosineSimilarity
- L1Distance
- EuclideanDistance
Record
abstract class which contains attributes and class label
- TrainRecord: + distance
- TestRecord: + predictedLabel
knn
the main class implementing the KNN algorithm
- For each TestRecord, use a k-size container (like a heap) to maintain k TrainRecords which are the nearest to that Testrecord while going through all TrainRecords. (No need to store all the distances which is a kind of wasting memory)
- During the classification phase, weigh the vote according to distance and assign the class lable with the largest weight.
- Whether there is need to think about outlier???
- If there are similar labels, enlarge the container's size dynamically

Questions:

When normalizing data, do we need to consider TrainingData and TestData together or seperately?
For different datasets, the max and min values might be different. As a result, the normalization results for TrainData and TestData are not unified.
The Prof. said that the test data set is normalized according to the scheme deduced by training set.
During KNN, is there a need to set a threshold to test outliers?
Should be No since there are so many different datasets. We may nesure that all test cases have valid class labels.

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