Abstract
Data mining is a dynamic and attractive research domain that has become known to discover information from the vast amount of constantly created data. Clustering is an unsupervised approach to data mining in which a group of similar items is assembled in one cluster. The quality of documents retrieved within a lesser amount of time has always been a fundamental problem in web document clustering. The authors introduce similarity technique-based K-means clustering using bee swarm optimization and artificial neural networks in this work. The artificial neural network helps classify the best centroid location based on the similarity index of the document and according to the trained structure of ANN to organize the best cluster number to test queries. The quality of papers returned is improved significantly with lesser execution time and improved efficiency through the projected method.
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Madaan, V., Munjal, K., Verma, S., Jhanjhi, N.Z., Singh, A. (2021). An Enhanced Cos-Neuro Bio-Inspired Approach for Document Clustering. In: Peng, SL., Hsieh, SY., Gopalakrishnan, S., Duraisamy, B. (eds) Intelligent Computing and Innovation on Data Science. Lecture Notes in Networks and Systems, vol 248. Springer, Singapore. https://doi.org/10.1007/978-981-16-3153-5_54
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DOI: https://doi.org/10.1007/978-981-16-3153-5_54
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