Kanimozhi K.V

Venkatesan M.

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

A Novel Map-Reduce Based Augmented Clustering Algorithm for Big Text Datasets

Advances in Intelligent Systems and Computing Data Engineering and Intelligent Computing

Text 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 International Journal of Electrical Engineering & Education

An enlarged map-reduce using 2logmean-PSO optimization for unstructured data

Advances in Intelligent Systems and Computing Advanced Computational and Communication Paradigms

Text Document Analysis Using Map-Reduce Framework

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

A Delaunay diagram-based Min-Max CP-Tree algorithm for Spatial Data Analysis

International Journal of Communication Networks and Distributed Systems

A novel Cp-Tree-based co-located classifier for big data analysis

Co-location pattern analysis represents the subsets of spatial events whose instances are found in close geographic proximity. Given a collection of Boolean spatial features, the co-location pattern discovery process finds the subsets of features frequently located together. Key challenges in co-location pattern analysis are modelling of neighbourhood in spatial domain, minimum prevalent threshold to generate collocation patterns and analysing extended spatial objects. We discuss the above key challenges using event centric approach and N-most prevalent co-location patterns approach. We propose a window-based model to find the neighbourhood for point spatial datasets and the multiple window model for extended spatial data objects. We also use N-most prevalent co-location patterns approach to filter the number of co-location pattern generation. We propose a more generic and efficient window-based model algorithm to find colocation patterns. Towards the end, we have done a comparative study of the existing approaches with our proposed approach. Copyright © 2013 Inderscience Enterprises Ltd.

Fulltext

International Journal of Computer Applications in Technology

A multiple window-based co-location pattern mining approach for various types of spatial data

2014 IEEE International Parallel & Distributed Processing Symposium Workshops

YAFIM: A Parallel Frequent Itemset Mining Algorithm with Spark

Journal	Data powered by TypesetAdvances in Intelligent Systems and Computing Data Engineering and Intelligent Computing
Publisher	Data powered by TypesetSpringer Singapore
ISSN	2194-5357
Open Access	0