Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{145639,
        author = {P.mahendra naidu and S.sajida},
        title = {An efficient Nearest Keyword Set Search In Multi Dimensional Datasets },
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {4},
        number = {10},
        pages = {666-668},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=145639},
        abstract = {In PC Data set examination, several documents are generally inspected. A significant part of the information in those documents comprises of unstructured content, whose investigation by PC analysts is hard to be performed. In existing framework, Location particular watchword inquiries on the web and in the GIS frameworks were prior addressed utilizing a blend of R-Tree and upset list. Felipe et al. created IR2-Tree to rank items from spatial datasets in light of a mix of their separations to the question areas and the significance of their content depictions to the inquiry catch phrases. In proposed, multi dimensional datasets where every datum point has an arrangement of watch words. The nearness of catch phrases in highlight space considers the advancement of new apparatuses to question and investigate these multi dimensional datasets. A NKS inquiry is an arrangement of client gave watchwords, and the consequence of the question may incorporate k sets of information focuses every one of which contains all the inquiry catchphrases and structures one of the best k most secure group in the multi dimensional space. we propose ProMiSH to empower quick preparing for NKS questions ProMiSH-E utilizes an arrangement of hash tables and transformed lists to play out a confined inquiry.},
        keywords = {IR2-Tree, Nearest Keyword Set Search, datasets},
        month = {},
        }