The Library
Fuzzy partition technique for clustering big urban dataset
Tools
Tools
Tools
Al Shami, Ahmad, Guo, Weisi and Pogrebna, Ganna (2016) Fuzzy partition technique for clustering big urban dataset. In: IEEE SAI Computing Conference, London, UK, 13-15 Jul 2016. Published in: 2016 SAI Computing Conference (SAI) pp. 1-5. ISBN 9781467384605.
|
PDF
WRAP-fuzzy-partition-clustering-big-Guo-2017.pdf - Accepted Version - Requires a PDF viewer. Download (962Kb) | Preview |
Official URL: http://doi.org/10.1109/SAI.2016.7555984
Abstract
Smart cities are collecting and producing massive amount of data from various data sources such as local weather stations, LIDAR data, mobile phones sensors, Internet of Things (IoT) etc. To use such large volume of data for potential benefits, it is important to store and analyse data using efficient and effective big data algorithms. However, this can be problematic due to many challenges. This article explores some of these challenges and tested the performance of two partition algorithms for clustering such Big Urban Datasets. Two handy clustering algorithms the K-Means vs. the Fuzzy c-Mean (FCM) were put to the test. The purpose of clustering urban data is to categorize it into homogeneous groups according to specific attributes. Clustering Big Urban Data in compact format represents the information of the whole data and this can benefit researchers to deal with this reorganised data much efficiently. To achieve this end, the two techniques were utilised against a large set of Lidar data to show how they perform on the same hardware set-up. Our experiments conclude that FCM outperformed the K-Means when presented with such type of dataset, however the latter is less demanding on the hardware utilisation.
| 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 Faculty of Science > WMG (Formerly the Warwick Manufacturing Group) |
||||||
| Library of Congress Subject Headings (LCSH): | Big data, Optical radar, Fuzzy systems | ||||||
| Journal or Publication Title: | 2016 SAI Computing Conference (SAI) | ||||||
| Publisher: | IEEE | ||||||
| ISBN: | 9781467384605 | ||||||
| Official Date: | 1 September 2016 | ||||||
| Dates: |
|
||||||
| Date of first compliant deposit: | 19 April 2016 | ||||||
| Date of first compliant Open Access: | 25 April 2017 | ||||||
| Page Range: | pp. 1-5 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access | ||||||
| Conference Paper Type: | Paper | ||||||
| Title of Event: | IEEE SAI Computing Conference | ||||||
| Type of Event: | Conference | ||||||
| Location of Event: | London, UK | ||||||
| Date(s) of Event: | 13-15 Jul 2016 |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
