IR-Assignment # 3

Implementation:

• Classify and Cluster Given Documents of BBC Sport using a Knn and K-means.

Files:

Jupyter Notebook:

The Notebook Contains a KNN and K-means Implementation.

• Check Notebook : IR-A3-K173654.ipynb

KNN Algorithm

Approach: Supervised

A k-nearest-neighbor is a data classification algorithm that attempts to determine what group a data point is in by looking at the data points around it.

An algorithm, looking at one point on a grid, trying to determine if a point is in group A or B, looks at the states of the points that are near it. The range is arbitrarily determined, but the point is to take a sample of the data. If the majority of the points are in group A, then it is likely that the data point in question will be A rather than B, and vice versa.

K-Mean Algorithm

Approach: Unsupervised

K-means clustering is a method used for clustering analysis, especially in data mining and statistics. It aims to partition a set of observations into a number of clusters (k), resulting in the partitioning of the data into Voronoi cells. It can be considered a method of finding out which group a certain object really belongs to.

Desktop System:

A GUI Based Executable is present that provides a Desktop Interface to the Django based web server which holds the implementation of KNN and Kmeans hosted on Heroku. It can classify and cluster documents and return results.

• Check Zip File For Desktop GUI Code : IR-SYS-A3

  • Repo Link: Click Here

• Check Folder For Desktop GUI Executable : IR-APP-A3

• Check Folder For Python Django Web Server : IR-SERVER-A3

  • Repo Link: Click Here

Web System:

A Web Application is hosted at https://ira3.netlify.com/#/ .

• Check Folder for Web Interface Code : IR-WEB-A3
  • Repo Link : Click Here

ScreenShots:

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References:

http://mlg.ucd.ie/datasets/bbc.html

Built Using:

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Template:

• electron react boilerplate