Please use this identifier to cite or link to this item: https://idr.l2.nitk.ac.in/jspui/handle/123456789/9886
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dc.contributor.authorKanimozhi, K.V.
dc.contributor.authorKrishnan, R.
dc.contributor.authorVenkatesan, M.
dc.date.accessioned2020-03-31T06:51:39Z-
dc.date.available2020-03-31T06:51:39Z-
dc.date.issued2019
dc.identifier.citationInternational Journal of Electrical Engineering Education, 2019, Vol., , pp.-en_US
dc.identifier.uri10.1177/0020720919894192
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/9886-
dc.description.abstractText clustering system is a proper technique which mainly segments large measure of textual documents into clusters. The size of the material influences the clustering of text by reducing its performance. In this manner, the textual document comprises sparse and uninformative features, and thus raises the computational time and decreases the execution of primary clustering process. Feature selection is a crucial system to choose another subset of instructive text feature to enhance text clustering execution and diminish computational time. The implemented model proposes a 2logmean-particle swarm optimization algorithm for the unstructured text clustering. In this newly proposed technique, all the texts are initially converted into ASCII value, and then by using the particle swarm optimization, the document text is clustered. The outcomes display that clustering accuracy of the implemented method is high compared to the existing K-means algorithm. Furthermore, performances of newly implemented techniques are evaluated concerning scalability, less computation speed with colossal dimensionality reduction. The Author(s) 2019.en_US
dc.titleAn enlarged map-reduce using 2logmean-PSO optimization for unstructured dataen_US
dc.typeArticleen_US
Appears in Collections:1. Journal Articles

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