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This book gathers selected high-quality research papers from the
International Conference on Computational Methods and Data
Engineering (ICMDE 2020), held at SRM University, Sonipat,
Delhi-NCR, India. Focusing on cutting-edge technologies and the
most dynamic areas of computational intelligence and data
engineering, the respective contributions address topics including
collective intelligence, intelligent transportation systems, fuzzy
systems, data privacy and security, data mining, data warehousing,
big data analytics, cloud computing, natural language processing,
swarm intelligence, and speech processing.
This book gathers selected high-quality research papers from the
International Conference on Computational Methods and Data
Engineering (ICMDE 2020), held at SRM University, Sonipat,
Delhi-NCR, India. Focusing on cutting-edge technologies and the
most dynamic areas of computational intelligence and data
engineering, the respective contributions address topics including
collective intelligence, intelligent transportation systems, fuzzy
systems, data privacy and security, data mining, data warehousing,
big data analytics, cloud computing, natural language processing,
swarm intelligence, and speech processing.
The book features original papers from International Conference on
Computational Methods and Data Engineering (ICCMDE 2021), organized
by School of Computer Science and Engineering, Vellore Institute of
Technology, Vellore, Tamil Nadu, India, during November 25-26,
2021. The book covers innovative and cutting-edge work of
researchers, developers, and practitioners from academia and
industry working in the area of advanced computing.
This report presents an integrated outlier detection method, which
is named "An Approach to Detect Outlier by Integrating Univariate
Outlier Detection and K-means Algorithm." It provides efficient
outlier detection and data clustering capabilities in the presence
of outliers, and based on filtering of the data after univariate
analysis. This algorithm is divided into two stages. The first
stage provides Univariate outlier analysis. The main objective of
the second stage is an iterative removal of objects, which are far
away from their cluster centroids by applying K-means algorithm.
The removal occurs according to the minimisation of the value of
sum of the distances of all the points to their respective centroid
in all the clusters. Finally, we provide experimental results from
the application of our algorithm on several datasets to show its
effectiveness and usefulness. The empirical results indicate that
the proposed method was successful in detecting outliers and
promising in practice.
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