Document-level semantic clustering. Unsupervised topic modelling.
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Updated
Feb 14, 2024 - Python
Document-level semantic clustering. Unsupervised topic modelling.
The thesis presents the parallelisation of a state-of-the art clustering algorithm, FISHDBC. This objective has been achived by improving the main data structures and components of the algorithm: HNSW, MST and HDBSCAN. My contribution is based on a lock-free strategy, completely wrote in Python.
HDBSCAN Tuning for BERTopic Models
Core Spanning Graph published in ICDE 2022
Implementation of statistics algorithms for Machine Learning & Data Mining. The algorithms were implemented with the Scikit-Learn Library
EIGEN FREQUENCY CLUSTERING USING [KMEANS] [KMEANS & PCA ] [DBSCAN] [HDBSCAN]
Optimize clustering labels using Silhouette Score.
Defines a boundary around cluster centers in a given point-layer shapefile.
NeuralMap is a data analysis tool based on Self-Organizing Maps
Data Science algorithms for Qlik implemented as a Python Server Side Extension (SSE).
Library and hand-made clustering algorithms are implemented in this project
Making word clouds more interesting
My solution for Kaggle NYC Taxi Fare Prediction ( ranked 21st/1463)
High Energy Physics particle tracking in CERN detectors
NLP on Korean news articles. Automatic topic extraction through dynamic clustering.
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