Mining K Nearest Neighbours

KNN is a supervised learning algorithm that can be used to identify the class for an unknown data instance based upon a similarity measure or a distance metric i.e. (KNN)

K-Nearest Neighbors (KNN)

K-Nearest Neighbors (KNN) is a simple, intuitive machine learning algorithm used for both classification and regression tasks. It is a type of instance-based learning, where the function is only approximated locally and all computation is deferred until classification.

How KNN Works

The KNN algorithm operates on the principle that similar data points tend to cluster together. When a new data point needs to be classified or evaluated, KNN looks at the ‘k’ closest data points in the feature space and uses their properties to make a prediction.

Key Concepts:

Distance Metric: The distance between data points is typically measured using Euclidean distance, but other metrics like Manhattan distance or Minkowski distance can also be used.

For two points $p$ and $q$ in Euclidean space, the Euclidean distance is given by: $d (p, q) = \sum_{i = 1}^{n} (p_{i} - q_{i})^{2}$
Choice of K: The number of nearest neighbors (k) is a critical parameter. A small value of k makes the model more sensitive to noise in the training data, while a large value of k makes the model less sensitive to relevant patterns.

Steps in KNN Algorithm

Choose the number of neighbors (k).
Calculate the distance between the new data point and all training data points.
Sort the distances and determine the k-nearest neighbors.
Aggregate the labels of the k-nearest neighbors to make a prediction (for classification, this is typically done by majority vote; for regression, it is done by averaging the values).

Applications of KNN

KNN is versatile and can be applied to various domains:

Classification: Used in text categorization, image recognition, and recommendation systems.
Regression: Used in predicting continuous values like house prices or stock market trends.

Advantages and Disadvantages

Advantages:

Simplicity: Easy to understand and implement.
Non-parametric: Makes no assumptions about the underlying data distribution.
Versatile: Can be used for both classification and regression tasks.

Disadvantages:

Computational Cost: High computational cost for large datasets due to the need to compute distances to all training points.
Memory Usage: Requires storing the entire training dataset.
Sensitivity to Irrelevant Features: Performance can degrade with high-dimensional data due to the curse of dimensionality.

Improvements and Variations

Several techniques can improve the performance of KNN:

Feature Scaling: Normalizing or standardizing features to ensure that distance calculations are meaningful.
Dimensionality Reduction: Using techniques like Principal Component Analysis (PCA) to reduce the number of features.
Weighted KNN: Assigning weights to neighbors based on their distance, giving closer neighbors more influence.

Conclusion

KNN is a powerful and flexible algorithm suitable for a wide range of applications. Its simplicity makes it a good starting point for many machine learning tasks, but care must be taken to choose the right value of k and to manage computational efficiency, especially with large datasets.

🪴 TJ's Notes 1.0

Explorer

Data Warehousing and Mining Lecture 11