The term weight is based on the frequency with which the term appears in that document. The term weighting scheme measures the importance of a term with respect to a document and a collection. A term with higher weight is more important than a term with lower weight. A document ranking model uses these term weights to find the rank of a document in a collection. We propose a cluster-based term weighting models based on the TF-IDF model. This term weighting model update the inter-cluster and intra-cluster frequency components uses the generated clusters as a reference in improving the retrieved relevant documents. These inter cluster and intra-cluster frequency components are used for weighting the importance of a term in addition to the term and document frequency components.
CITATION STYLE
Prakash, B. R., Hanumanthappa, M., & Mamatha, M. (2014). Cluster based term weighting model for web document clustering. In Advances in Intelligent Systems and Computing (Vol. 259, pp. 815–822). Springer Verlag. https://doi.org/10.1007/978-81-322-1768-8_70
Mendeley helps you to discover research relevant for your work.