This reference gets into mathmatical ways how KNN and K-Nearest Neighbors are different concepts

Umamaheswaran, Venkatesh. (2018, November 12). Comprehending K-means and KNN Algorithms. Medium. Retrieved From [becominghuman.ai/comprehending-k-means-and-knn-algorithms-c791be90883d](https://becominghuman.ai/comprehending-k-means-and-knn-algorithms-c791be90883d) 

University of Michigan - Ann Arbor

K-Means Clustering is a form of unsupervised learning that aims to identify distinct categories in data by segmenting the data by Euclidean Distance into 'k' number of clusters.

K-Means Clustering

K-nearest neighbors is a non-parametric statistical method, which predicts based on the *k* nearest neighbors. k-NN classifiers are an example of what's called instance based or memory based supervised learning.

It can be applied to both classification and regression problems.
- When applied to classification problems, the prediction result would be the most common one among the *k* nearest training observations.
- When applied to regression problems, the prediction result would be the average of the *k* nearest training observations.

Some key notes about this method:
- It makes few assumptions about structure of data
- It gives potentially accurate but sometimes unstable predictions 
- It is sensitive to small changes in training data



K-nearest Neighbors (k-NN)

How might K-means be used in conjunction with supervised methods to predict on an unlabeled data set?

Dhiraj, K. (2019, August 29). Difference between K-Nearest Neighbor(K-NN) and K-Means Clustering. Medium. [medium.com/@dhiraj8899/difference-between-k-nearest-neighbor-k-nn-and-k-means-clustering-d9a44859182f](https://medium.com/@dhiraj8899/difference-between-k-nearest-neighbor-k-nn-and-k-means-clustering-d9a44859182f) 

Medium: Difference between K-Means and KNN

Math/Python Explanation: Difference Between K-Means and KNN

1. Randomly assign a number, from 1 to K, to each of the observations. These serve as initial cluster assignments for the observations.
2. Iterate until the cluster assignments stop changing:
- (a) For each of the K clusters, compute the cluster centroid. The kth cluster centroid is the vector of the p feature means for the observations in the kth cluster.
- (b) Assign each observation to the cluster whose centroid is closest

Algorithm of K-means Clustering

This picture depicts an example of the K-Means Clustering process. Random data points are selected and serve as the initial clusters. How many there are depends on the value of k, which in this picture’s case, k = 2. It then measures the Euclidean distance between the 1st data point and the two initial clusters, and assigns it to whichever cluster is the closest. It repeats this process, calculates the mean values, and reclusters based on that. The process repeats until the cluster no longer changes (seen in final image).


Image of K-Means Clustering Process

1. Choosing K manually
2. Clustering data of varying sizes and density
3. Clustering outliers
4. Scaling with number of dimensions
5. Quantifying how well a data cluster corresponds to reality

Limitations of K-means clustering

1. Relatively simple to implement
2. Scales to large data sets 
3. Guarantees convergence
4. Can warm-start the positions of centroids
5. Easily adapts to new examples
6. Generalizes to clusters of different shapes and sizes, such as elliptical clusters
7. The principle is relatively simple, the implementation is also very easy, and the convergence speed is fast. 
8. The clustering effect is better. 
9. The interpretability of the algorithm is strong. 
10. The main parameter to be adjusted is only the number of clusters K.

Advantages of K-means clustering

There are two main waits to pick the optimal k value for k-means clustering: 

1. Elbow method: Plot the explained variation as a function of the number of clusters and pick the elbow of the curve as the number of clusters to use.

2. Silhouette score: Calculated using the silhouette coefficient - (x-y)/max(x,y) where x is mean distance to the instances of the next closest cluster and y is the mean distances to the other instances in the same cluster. You pick the k value with the biggest silhouette score.

Picking optimal k value

One popular way to determine the optimal number of k is by using the elbow method. To use this method, you will run K means n times, each time adding another cluster. After each iteration you graph a score for that number of k. The point in the graph where there is an “elbow” is the point of optimal clusters. The picture below shows an example graph that would be used for determining the number of clusters -- in this example case 3 clusters is optimal


The Elbow Method for Selecting Optimal K

Hands-On Machine Learning with R: Chapter 20 K-means Clustering
https://bradleyboehmke.github.io/HOML/kmeans.html

Hands-On Machine Learning with R: Chapter 20 K-means Clustering

Statquest with Josh Starmer.(2017, June 26). Statquest: K-nearest neighbors, Clearly Explained [Video]. Youtube.  [www.youtube.com/watch?v=HVXime0nQeI](https://www.youtube.com/watch?v=HVXime0nQeI) 

Reference Video: K-Nearest Neighbors

A good source that talks about KNN, it's various uses, its algorithm, and examples.

Harrison, O. (2019, July 14). Machine Learning Basics with the K-Nearest Neighbors Algorithm. Retrieved March 12, 2020, from [towardsdatascience.com/machine-learning-basics-with-the-k-nearest-neighbors-algorithm-6a6e71d01761](https://towardsdatascience.com/machine-learning-basics-with-the-k-nearest-neighbors-algorithm-6a6e71d01761) 

Machine Learning Basics with KNN Algorithm

When using KNN for regression problems, the model is based off of ‘feature similarity’ which means that the output value of an observation you are trying to find is based on how closely it resembles the points in the training data set. With regression using KNN, this similarity is based on the average distance, typically using Euclidean distance, between the chosen observation and those around it.

KNN Regression

Singh, A. (2019, August 9). A Practical Introduction to K-Nearest Neighbor for Regression. Retrieved March 18, 2020, from https://www.analyticsvidhya.com/blog/2018/08/k-nearest-neighbor-introduction-regression-python/

A Practical Introduction to K-Nearest Neighbors Algorithm for Regression (Reference)

KNN can be useful in solving problems that have solutions that depend on identifying similar objects.
1. Recommender Systems
For example: Recommending products on Amazon, articles on Medium, movies on Netflix, or videos on YouTube.
2. Concept Search
This is used to search for semantically similar documents (i.e., documents containing similar topics). We could find it in many e-Discovery software packages, which are used for helping companies find all of the e-mails, contracts, etc.
3. Herta Security
They then use k-NN to identify a person by compare the face to their watchlist. 

KNN in practice

The UM online course: Applied Machine Learning in Python->Week 2-> K-Nearest Neighbors: Classification and Regression. The link is: https://www.online.umich.edu/courses/applied-machine-learning-in-python/



Reference video: K-Nearest Neighbors: Classification and Regression

Scikit-learn enables the creation of classifier objects. For example, 
```python
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors = 5, weights = 'distance')
```
In this example, the classifier object (named knn) will consider 5 neighbors in its kneighbors queries.   Setting the weights parameter to 'distance' means that closer neighbors will be more influential than those farther away  (in terms of detemrining which category the "target" point belongs to).

sklearn.neighbors.KNeighborsClassifier

K-nearest neighbors can be used to classify data. Given a training set X_train with labels y_train, and given a new instance x_test to be classified:

- Find the k most similar instances (X_NN) to x_test that are in X_train.
- Get the labels y_NN for the instances in X_NN.
- Predict the label for x_test by combining the labels y_NN. The combination can be done through different methods, e.g., a simple majority vote can find the label that is the most common among the k most similar examples.

For example, if K = 1, then the first closest neighbor will be used to classify the unknown data point. If K = 14, then the 14 nearest neighbors will be used. If the data point falls between multiple clusters, then you choose the category that holds the most.

Classification Algorithm of K-Nearest Neighbors

Advantages
- It is easy and simple to implement
- Since it is non-parametric, you don’t have to make any assumptions about $f$
- A model does not need to be created
- It can be used for various statistical methods (classification, regression, etc.)

Disadvantages
- As the number of predictors increase, KNN can get very slow and at some point, not usable anymore.


K-Nearest Neighbors Advantages and Disadvantages

Used the data for classes 0, 1, and 2 plotted in the image.

What class would a KNeighborsClassifer classify the new point as for k = 1 and k = 3?

Which of the following is true for the nearest neighbor classifier?

The $$1$$-nearest neighbor algorithm is the simplest case of the $$k$$-nearest neighbor approach, where $$k=1$$. At training time, the learner memorizes the dataset, and at prediction time, it assigns a new data point the label of its single closest neighbor based on a chosen distance function $$d$$. Because it memorizes the data, the algorithm always achieves a training error of zero by perfectly interpolating the training data. Despite this, it can still generalize; under mild conditions, the $$1$$-nearest neighbor algorithm is a consistent estimator, meaning it eventually converges to the optimal predictor.

1-Nearest Neighbor Algorithm

In nonparametric methods like the $$k$$-nearest neighbor algorithm, a distance function, denoted as $$d$$, must be specified to measure the similarity between data points. Equivalently, this involves defining a vector-valued basis function $$\phi(\mathbf{x})$$ for featurizing the data. The choice of distance metric is critical because it encodes different inductive biases and represents specific assumptions about the underlying data patterns. With a finite amount of available data, different distance functions will yield different predictors depending on how compatible these assumptions are with the observed data.

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