Abstract
Clustering is an unsupervised learning technique, grouping a set of objects into subsets or clusters. It forms the clusters that are similar with the data points internally, but dissimilar with the data points that are present in other clusters from each other. Extraction of data efficiently and effectively from the datasets or data holders need enhanced mechanism. Extraction of relevant sentences based on user query plays a big role in data mining and web mining etc. In this paper we propose an efficient and effective way to extract sentences by taking query as input and forming hierarchical clustering with cosine similarity measure. A Threshold value is taken initially, and clusters are divided depending on it. Further clustering is done based on the previous Threshold value.
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Kavyasrujana, D., Rao, B.C. (2015). Hierarchical Clustering for Sentence Extraction Using Cosine Similarity Measure. In: Satapathy, S., Govardhan, A., Raju, K., Mandal, J. (eds) Emerging ICT for Bridging the Future - Proceedings of the 49th Annual Convention of the Computer Society of India (CSI) Volume 1. Advances in Intelligent Systems and Computing, vol 337. Springer, Cham. https://doi.org/10.1007/978-3-319-13728-5_21
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DOI: https://doi.org/10.1007/978-3-319-13728-5_21
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13727-8
Online ISBN: 978-3-319-13728-5
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