Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Nowadays there is an important need by journalists and media monitoring companies to cluster news in large amounts of b articles, in order to ensure fast access to their topics or events of interest. Our aim in this work is to identify groups of news articles that share a common topic or event, without a priori knowledge of the number of clusters. The estimation of the correct number of topics is a challenging issue, due to the existence of “noise”, i.e. news articles which are irrelevant to all other topics. In this context,  introduce a novel density-based news clustering framework, in which the assignment of news articles to topics is done by the ll-established Latent Dirichlet Allocation, but the estimation of the number of clusters is performed by our novel method, called “DBSCAN-Martingale”, which allows for extracting noise from the dataset and progressively extracts clusters from an OPTICS reachability plot.  evaluate our framework and the DBSCAN-Martingale on the 20newsgroups-mini dataset and on 220 b news articles, which are references to specific Wikipedia pages. Among tnty methods for news clustering, without knowing the number of clusters k, the framework of DBSCAN-Martingale provides the correct number of clusters and the highest Normalized Mutual Information.
['Ilias Gialampoukidis', 'Stefanos Vrochidis', 'Ioannis Kompatsiaris']
I am writing a research paper on a idea A hybrid framework for news clustering based on the DBSCAN-Martingale and LDA
[{"from": "human", "value": "I am writing a research paper on a idea A hybrid framework for news clustering based on the DBSCAN-Martingale and LDA"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Nowadays there is an important need by journalists and media monitoring companies to cluster news in large amounts of b articles, in order to ensure fast access to their topics or events of interest. Our aim in this work is to identify groups of news articles that share a common topic or event, without a priori knowledge of the number of clusters. The estimation of the correct number of topics is a challenging issue, due to the existence of \u201cnoise\u201d, i.e. news articles which are irrelevant to all other topics. In this context,  introduce a novel density-based news clustering framework, in which the assignment of news articles to topics is done by the ll-established Latent Dirichlet Allocation, but the estimation of the number of clusters is performed by our novel method, called \u201cDBSCAN-Martingale\u201d, which allows for extracting noise from the dataset and progressively extracts clusters from an OPTICS reachability plot.  evaluate our framework and the DBSCAN-Martingale on the 20newsgroups-mini dataset and on 220 b news articles, which are references to specific Wikipedia pages. Among tnty methods for news clustering, without knowing the number of clusters k, the framework of DBSCAN-Martingale provides the correct number of clusters and the highest Normalized Mutual Information."}]