scispace - formally typeset
V

Vasudha Bhatnagar

Researcher at Dept. of Computer Science, University of Delhi

Publications -  72
Citations -  1313

Vasudha Bhatnagar is an academic researcher from Dept. of Computer Science, University of Delhi. The author has contributed to research in topics: Cluster analysis & Association rule learning. The author has an hindex of 16, co-authored 70 publications receiving 1022 citations. Previous affiliations of Vasudha Bhatnagar include University of Delhi & South Asian University.

Papers
More filters
BookDOI

Big Data Analytics

TL;DR: The latest disruptive trends and developments in digital age comprise social networking, mobility, analytics and cloud, popularly known as SMAC, which saw Big Data Technologies being leveraged to power business intelligence applications in 2016.
Journal ArticleDOI

Results from the Supernova Photometric Classification Challenge

TL;DR: The Supernova Photometric Classification Challenge (SNPhotCC) as mentioned in this paper was the first challenge for the classification of simulated supernovae (SNe), with types (Ia, Ibc and II) selected in proportion to their expected rates.
Journal ArticleDOI

An overview of the commercial cloud monitoring tools: research dimensions, design issues, and state-of-the-art

TL;DR: In this article, the authors identify and discuss the major research dimensions and design issues related to engineering cloud monitoring tools and further discuss how the aforementioned research dimensions are handled by current academic research as well as by commercial monitoring tools.
Book ChapterDOI

K-means Clustering Algorithm for Categorical Attributes

TL;DR: The quality of clusters produced depends on the initialization of clusters and the order in which the algorithm is based on the K-means philosophy but removes the numeric data limitation.
Journal ArticleDOI

The impact of data mining techniques on medical diagnostics

TL;DR: The impact of data mining techniques, including artificial neural networks, on medical diagnostics is examined and a few areas of healthcare where these techniques can be applied to healthcare databases for knowledge discovery are identified.