Incremental-News-Clustering | incremental clustering system

Incremental-News-Clustering is a Python library typically used in Telecommunications, Media, Advertising, Marketing applications. Incremental-News-Clustering has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However Incremental-News-Clustering build file is not available. You can download it from GitHub.

The goal was to research model-based clustering methods, notably the Distance Dependent Chinese Restaurant Process (ddCRP), and propose an incremental clustering system which would be capable of maintaining the growing number of topic clusters of news articles coming online from a crawler. LDA, LSA, and doc2vec methods were used to represent a document as a fixed-length numeric vector. Cluster assignments given by a proof-of-concept implementation of such a system were evaluated using various metrics, notably purity, F-measure and V-measure. A modification of V-measure -- NV-measure -- was introduced in order to penalize an excessive or insufficient number of clusters. The best results were achieved with doc2vec and ddCRP. Due to copyright, news articles used for experiments are only available at the university library. Full thesis text: thesis.pdf Poster: Vana_Martin_2018.pdf.