@inproceedings{9bd2c4dfda514ed9bf46035335d7616f,
title = "Multi-perspective ant colony optimization for mining and understanding the topology oriented big data",
abstract = "Big data has the potential to transform how data can be used to manipulate, gather, collect, enforce and contribute towards data centric applications. Data can have meaning and big data has meaning hidden inside it which can be used to empower multiple applications. Big data has its challenges; this massive amount of data can be used to extract a few lines or a few million lines that actually serve the purpose and meaning. For such outputs the algorithmic techniques needs to be devised that can efficiently extract meaning from a topological shape based on the nature of application and the information that is being looked for. The search for hidden meaning in topology of data is a challenge. Many techniques are employed to discover the hidden meaning and patterns in the data. This paper presents an algorithm for mining the clustered topological data using ant colony optimization.",
keywords = "Ant Colony, Big Data, Clustering, Optimization, Topology",
author = "Khalid Hiba and Qamar Usman and Hameed Mazhar",
year = "2017",
language = "English",
series = "Lecture Notes in Engineering and Computer Science",
publisher = "Newswood Limited",
pages = "211--214",
editor = "Hukins, \{David WL\} and Korsunsky, \{A. M.\} and Len Gelman and Ao, \{S. I.\} and Andrew Hunter",
booktitle = "Proceedings of the World Congress on Engineering 2017, WCE 2017",
}