Performance analysis of hard clustering techniques for big IoT data analytics

Mohiuddin Ahmed, Abu Barkat

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

2 Citations (Scopus)

Abstract

Data analytics for Internet of Things (IoT) is an important task in today's connected environment. In particular, identification of infrequent patterns from a huge amount of data is certainly a challenging task. Clustering is a well established technique to divulge the patterns from any given dataset. However, one of the impediments for clustering is to provide the number of clusters that most of the clustering algorithm requires, for example the famous k-means requires the value of k (number of clusters to be produced). GenClust++ and x-means clustering algorithms can automatically identify the number of clusters unlike other hard clustering algorithms. In this paper, we investigate the effectiveness of these two algorithms to identify infrequent patterns or the anomalous clusters. We experimented with seven benchmark IoT datasets and it is evident that the performance of x-means in terms of TPR, FPR is better than GenClust++. In addition to that, in terms of the computational efficiency, x-means outperforms the GenClust++.

Original languageEnglish
Title of host publicationProceedings - 2019 Cybersecurity and Cyberforensics Conference, CCC 2019
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages62-66
Number of pages5
ISBN (Electronic)9781728126005
ISBN (Print)9781728126005
DOIs
Publication statusPublished - 1 May 2019
Externally publishedYes
Event2019 Cybersecurity and Cyberforensics Conference, CCC 2019 - Melbourne, Australia
Duration: 7 May 20198 May 2019

Publication series

NameProceedings - 2019 Cybersecurity and Cyberforensics Conference, CCC 2019

Conference

Conference2019 Cybersecurity and Cyberforensics Conference, CCC 2019
Country/TerritoryAustralia
CityMelbourne
Period7/05/198/05/19

Fingerprint

Dive into the research topics of 'Performance analysis of hard clustering techniques for big IoT data analytics'. Together they form a unique fingerprint.

Cite this