Clustering News Articles For Topic Detection

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@article{1700671,
      author = {Vaidehi Patel, Arpita Patel},
      title = {Clustering News Articles For Topic Detection},
      journal = {Iconic Research And Engineering Journals},
      year = {2018},
      volume = {1},
      number = {11},
      pages = {57-61},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1700671.pdf},
      abstract = {In this paper we presented an approach for detecting topics from news articles. Topic detection used in text mining process. Text mining is a field that extract previously unknown and useful information from unstructured textual data. The main purpose of topic detection and tracking is to identify and follow events presented in multiple news sources. Topic detection and tracking would be very helpful to have a system able to map out the data automatically finding story boundaries, determining what stories go with one another, and discovering when something new has happened. We would try to find the first story of new events, identifying all subsequent stories on a certain topic defined by a small number of sample stories, and detect the occurrence of new events. We are going to use agglomeration clustering based on average linkage for detecting the topics.},
      keywords = {Topic detection, Text Mining, agglomeration clustering.},
      month = {May},
  }