Case study on cancer with multiple clustering techniques
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Updated
Feb 13, 2021 - Jupyter Notebook
Case study on cancer with multiple clustering techniques
Implementation of data science and machine learning concepts
The aim of this project is to implement k-mediods algorithm of unsupervised learning from scratch. 3 random numpy arrays(2-D) have been taken into consideration for this project. This code can be used to partition any given dataset into 'n' clusters where n can be any real number of user's choice.
This is a modified version of the Webpage-Similarity project. With the addition of 190 more wikipedia pages, a more efficient method of data management is required. The main focus of this project is to create clusters, use persistent data stores and extendible hashing for quick data retrieval.
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