Clustering of large dataset using adaptive resonance theory 2 (Art 2)
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Lautech Journal of engineering and Technology
Each and every day we generate a vast amount of data from cloud, mobile and social technology, consequently, there is a need to make sense out of this data. However, mining of big data comes with its own problem the major problem being the difficulty in detecting patterns in evolving data. Clustering techniques can lead us to discover hidden information in these evolving data. Traditional data clustering models such as K-means do not scale well on large data set and are dependent on assumptions regarding cluster statistical properties (ie. Number of clusters, cluster shape), while unsupervised ANN algorithms (Adaptive Resonance Theory – ART networks) are recognized widely by their ability to discover hidden patterns, adapt to evolving large data and robustness to noise. Consequently, in this paper, Adaptive Resonance Theory 2(ART2) was used to address the problem of clustering large data set using a sensor stream data for the clustering experiments. The data was categorized into 40 categories or clusters close to the 54 class of the data with initial parameters of vigilance parameter = 0.9. The vigilance parameter was varied to study the evolution of the number of categories. The pattern match (clusters) gets finer when the vigilance parameter is closer to 1 and coarser when it is away from 1. The study revealed that the closer the value of the vigilance parameter to 1, the more number of clusters (categories) produced and the farther the value of the vigilance parameter the less the number of clusters.
Data Mining , Large Dataset , Big Data , Machine Learning , Neural Network , Cluster Algorithm , Adaptive System , Adaptive Resonance Theory , Research Subject Categories::TECHNOLOGY::Information technology::Computer science::Computer science
Adewole, A.P. and Amuda, T.J. (2014). Clustering of large dataset using adaptive resonance theory 2 (Art 2). Lautech Journal of engineering and Technology, Vol.8(2): 10-14pp.