Application of KNN Algorithm in Machine Learning

Application of KNN Algorithm:

1. Nearest Neighbor-based Content Retrieval: This is one of the fascinating applications of Knn. Basically, we can use Computer Vision for many cases. You can consider handwriting detection as a rudimentary nearest-neighbor problem. The problem becomes more fascinating if the content is a video.

2. Gene Expression: This is another cool area where many a time, Knn performs better than other state-of-the-art techniques. In fact, a combination of KNN-SVM is one of the most popular techniques there.

3. Protein-protein interaction and 3D structure prediction: Graph-based KNN is used in protein interaction. Similarly, KNN is used in structure prediction.

4. Text-mining: The Knn algorithm is one of the most popular algorithms for text categorization or text mining. Some of the most recent works on this topic are for instance. Different numbers of nearest neighbors are used for different classes in this approach, rather than a fixed number across all classes. In this way, the only parameter that needs to be chosen by the user when using Knn. The K value becomes less sensible and hence it doesn’t need to be carefully chosen as in the standard algorithm.

5. Agriculture: In general, Knn is applied less than other data mining techniques in agriculture-related fields. It has been applied, for instance, for stimulating daily precipitations and other weather variables. Another interesting application is the evaluation of forest inventories and for estimating forest variables. In these applications, satellite imagery is used, with the aim of mapping the land cover and land use with a few discrete classes.

6. Finance: Data mining is a process of discovering useful patterns and correlation has its own niche in financial modeling. Similar to other computational methods almost every data mining method and technique has been used in financial modeling. An incomplete list includes a variety of linear and non-linear models multi-layer neural networks, k-means and hierarchical clustering, k-nearest neighbors, decision tree analysis, regression, general multiple regression, principal component analysis, and Bayesian learning.

7. Medicine:Predict whether a patient, hospitalized due to a heart attack will have a second heart attack. The prediction is to be based on demographic, diet, and clinical measurements for that patient. Estimate the amount of glucose in the blood of a diabetic person, from the infrared absorption spectrum of that person’s blood. Identify the risk factors for prostate cancer based on clinical and demographic variables. The Knn algorithm has been also applied for analyzing microarray gene expression data, where the Knn algorithm has been coupled with generic algorithms which are used as a search tool.