Publication Type

Journal Article

Version

acceptedVersion

Publication Date

12-2015

Abstract

The large scale and complex nature of social media data raises the need to scale clustering techniques to big data and make them capable of automatically identifying data clusters with few empirical settings. In this paper, we present our investigation and three algorithms based on the fuzzy adaptive resonance theory (Fuzzy ART) that have linear computational complexity, use a single parameter, i.e., the vigilance parameter to identify data clusters, and are robust to modest parameter settings. The contribution of this paper lies in two aspects. First, we theoretically demonstrate how complement coding, commonly known as a normalization method, changes the clustering mechanism of Fuzzy ART, and discover the vigilance region (VR) that essentially determines how a cluster in the Fuzzy ART system recognizes similar patterns in the feature space. The VR gives an intrinsic interpretation of the clustering mechanism and limitations of Fuzzy ART. Second, we introduce the idea of allowing different clusters in the Fuzzy ART system to have different vigilance levels in order to meet the diverse nature of the pattern distribution of social media data. To this end, we propose three vigilance adaptation methods, namely, the activation maximization (AM) rule, the confliction minimization (CM) rule, and the hybrid integration (HI) rule. With an initial vigilance value, the resulting clustering algorithms, namely, the AM-ART, CM-ART, and HI-ART, can automatically adapt the vigilance values of all clusters during the learning epochs in order to produce better cluster boundaries. Experiments on four social media data sets show that AM-ART, CM-ART, and HI-ART are more robust than Fuzzy ART to the initial vigilance value, and they usually achieve better or comparable performance and much faster speed than the state-of-the-art clustering algorithms that also do not require a predefined number of clusters.

Keywords

Clustering, Big social media data, Adaptive Resonance Theory, Vigilance region, Adaptive parameter tuning

Discipline

Computer and Systems Architecture | Databases and Information Systems | OS and Networks

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Neural Networks and Learning Systems

Volume

27

Issue

12

First Page

2656

Last Page

2669

ISSN

2162-237X

Identifier

10.1109/TNNLS.2015.2498625

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TNNLS.2015.2498625

Download

Share

COinS