Publication Type : Conference Paper
Publisher : Springer, Singapore
Source : Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 1348. Springer, Singapore
Url : https://link.springer.com/chapter/10.1007/978-981-19-4676-9_27
Campus : Amritapuri
School : School of Computing
Department : Computer Science and Engineering
Year : 2023
Abstract : The advancement in the technology rises online unstructured data. As the data grow rapidly, tackling the information is becoming hard. There is a demand to maintain these unstructured data to gather important insights. Clustering of the text documents has become leading edge over Internet. Document clustering is mainly described as grouping of the similar documents. It plays vital role for establishing massive information. The paper shows an overview of study done on different clustering algorithms on covid data. The study of the semantic links between words and concepts in texts aids in the classification of documents based on their meaning and conception. The clusters were visualized using the k-means clustering technique, which was then evaluated using t-distributed stochastic neighbor embedding (t-SNE) and principal component analysis (PCA).
Cite this Research Publication : Suresh, S., Krishna, G., Thushara, M.G. (2023). Study of Document Clustering Algorithms Applied on Covid Data. In: Dutta, P., Bhattacharya, A., Dutta, S., Lai, WC. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 1348. Springer, Singapore