Abstract
In this world the Internet has become very casual for searching. Ordinary user appears to use it every time; even they need to search keyword from any query information. Also, people use search engine like Google, Bing when they are willing to search something, wants to use some relevant information or go to their synonyms. But searching for correct result requires more time and less execution speed even they produce multiple choices. So, this process is very confusing for users to decide one correct keyword amid the many results as a search engine show overall results. An agglomerative algorithm which is useful in searching better result by their centroid, hence it executes up to their centroid. Intended for the Bisect K means approach is used which aim to generate exact keyword in less time and reducing computational cost. For these reasons, enhance method called Bisect k means approach is very useful for knowing the best result from requiring query candidate.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Chen, Y., Wang, W., Liu, Z., Lin, X.: Keyword search on structured and semi-structured data, In: SIGMOD Conference, pp. b1005–1010 (2009)
Guo, L., Shao, F., Botev, C., Shanmugasundaram, J.: XRANK: ranked keyword search over XML documents. In: SIGMOD Conference, pp. 16–27 (2003)
Sun, C., Chan, C.Y., Goenka, A.K.: Multiway SLCA based keyword search in XML data. In: WWW, pp. 1043–1052 (2007)
Xu, Y., Papakonstantinou, Y.: Efficient keyword search for smallest LCAs in xml databases. In: SIGMOD Conference, pp. 537–538 (2005)
Clarke, C.L.A.: Novelty and diversity in information retrieval evaluation. In: SIGIR, pp. 659–666 (2008)
Demidova, E., Fankhauser, P., Zhou, X., Nejdl, W.: DivQ: diversification for keyword search over structured databases. In: Proceedings SIGIR, pp. 331–338 (2010)
Zhuang, Y., Mao, Y., Chen, X.: A limited-iteration isect K-means for fast clustering large datasets. In: IEEE TrustCom-BigDataSE-ISPA, pp. 2257–2262 (2016)
Chandan, K., Raddy, A.: Survey of partitional and hierarchical clustering algorithm, pp. 57–110
Murugesan, K. Zhang, J.: Hybride bisect K-means clustering algorithm. In: International Conference on Business Computing and Global Information (2011)
Slonim, N. Tishby, N.: Agglomerative Aglorithm Bottlneck
Sasirekha, K., Baby, P.: Agglomerative hierarchical clustering algorithm: a review. IJSRP 3(3), 1–3 (2013)
Hasan, M., Mueen, A., Tsotras, V.J., Keogh, E.J.: Diversifying query results on semi-structured data. In: CIKM, pp. 2099–2103 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
More, D.P., Patil, U.M. (2018). Searching Lead to Better Search Intension for Keyword. In: Deshpande, A., et al. Smart Trends in Information Technology and Computer Communications. SmartCom 2017. Communications in Computer and Information Science, vol 876. Springer, Singapore. https://doi.org/10.1007/978-981-13-1423-0_14
Download citation
DOI: https://doi.org/10.1007/978-981-13-1423-0_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1422-3
Online ISBN: 978-981-13-1423-0
eBook Packages: Computer ScienceComputer Science (R0)