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Clustering big urban data sets

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Al Shami, Ahmad, Guo, Weisi and Pogrebna, Ganna (2015) Clustering big urban data sets. In: IEEE International Smart Cities Conference, Guadalajara, Mexico, 25-28 Oct 2015 (Unpublished)

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Abstract

Cities are producing and collecting massive amount of data from various sources such as transportation network, energy sector, smart homes, tax records, surveys, LIDAR data, mobile phones sensors etc. All of the aforementioned data, when connected via the Internet, fall under the Internet of Things (IoT) category. To use such a large volume of data for potential scientific computing benefits, it is important to store and analyze such amount of urban data using efficient computing resources and algorithms. However, this can be problematic due to many challenges. This article explores some of these challenges and test the performance of two partitional algorithms for clustering Big Urban Datasets, namely: the K-Means vs. the Fuzzy cMean (FCM). Clustering Big Urban Data in compact format represents the information of the whole data and this can benefit researchers to deal with this reorganized data much efficiently. Our experiments conclude that FCM outperformed the K-Means when presented with such type of dataset, however the later is lighter on the hardware utilisations.

Item Type: Conference Item (Paper)
Subjects: Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Science, Engineering and Medicine > Engineering > Engineering
Library of Congress Subject Headings (LCSH): Big data, Cluster analysis -- Computer programs, Internet of things
Official Date: 2015
Dates:
DateEvent
2015Available
Status: Peer Reviewed
Publication Status: Unpublished
Conference Paper Type: Paper
Title of Event: IEEE International Smart Cities Conference
Type of Event: Conference
Location of Event: Guadalajara, Mexico
Date(s) of Event: 25-28 Oct 2015
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