Fuzzy Partition Technique for Clustering Big Urban Dataset - Ahmad AlShami
Conference Website: http://saiconference.com/Computing
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.
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.