A Hash-Based Approach for Document Retrieval by Utilizing Term Features

Gupta, R. K.; Patel, Durga; Bramhe, Ankit

doi:10.1007/978-981-10-8055-5_55

Cited by 2 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Even though the ambiguity was resolved, it retrieved the unrelated documents. Gupta et al [22] developed a hash-based indexing approach for document retrieval. This approach provided the privacy to retrieve the document based on the term features.…”

Section: Literature Surveymentioning

confidence: 99%

An approach for document retrieval using cluster-based inverted indexing

Chandwani

Ahlawat²,

Dubey³

2021

Journal of Information Science

View full text Add to dashboard Cite

Document retrieval plays an important role in knowledge management as it facilitates us to discover the relevant information from the existing data. This article proposes a cluster-based inverted indexing algorithm for document retrieval. First, the pre-processing is done to remove the unnecessary and redundant words from the documents. Then, the indexing of documents is done by the cluster-based inverted indexing algorithm, which is developed by integrating the piecewise fuzzy C-means (piFCM) clustering algorithm and inverted indexing. After providing the index to the documents, the query matching is performed for the user queries using the Bhattacharyya distance. Finally, the query optimisation is done by the Pearson correlation coefficient, and the relevant documents are retrieved. The performance of the proposed algorithm is analysed by the WebKB data set and Twenty Newsgroups data set. The analysis exposes that the proposed algorithm offers high performance with a precision of 1, recall of 0.70 and F-measure of 0.8235. The proposed document retrieval system retrieves the most relevant documents and speeds up the storing and retrieval of information.

show abstract

Section: Literature Surveymentioning

confidence: 99%