Cluster Based Term Weighting Model for Web Document Clustering

Autor:	M. Hanumanthappa, M. Mamatha, B. R. Prakash
Rok vydání:	2014
Předmět:	Information retrieval Computer science Computer Science::Information Retrieval Rank (computer programming) Correlation clustering Document clustering computer.software_genre Weighting Ranking (information retrieval) Term (time) Data mining Cluster analysis computer k-medians clustering
Zdroj:	Advances in Intelligent Systems and Computing ISBN: 9788132217671 SocProS (2)
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::c3588b91678cecc7c1514b8640fabfce https://doi.org/10.1007/978-81-322-1768-8_70 Zobrazit plný text záznamu