Open AccessScalable density based spatial clustering with integrated one-class SVM for noise reduction
Author(s) -
Khalil Ahmed,
T. Abdul Razak
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.9.10093
Subject(s) - dbscan , computer science , cluster analysis , support vector machine , scalability , pattern recognition (psychology) , artificial intelligence , noise reduction , data mining , noise (video) , spatial analysis , machine learning , classifier (uml) , unsupervised learning , fuzzy clustering , cure data clustering algorithm , mathematics , image (mathematics) , database , statistics
Information extraction from data is one of the key necessities for data analysis. Unsupervised nature of data leads to complex computational methods for analysis. This paper presents a density based spatial clustering technique integrated with one-class SVM, a machine learning technique for noise reduction, a modified variant of DBSCAN called NRDBSCAN. Analysis of DBSCAN exhibits its major requirement of accurate thresholds, absence of which yields suboptimal results. However, identifying accurate threshold settings is unattainable. Noise is one of the major side-effects of the threshold gap. The proposed work reduces noise by integrating a machine learning classifier into the operation structure of DBSCAN. Further, the proposed technique is parallelized using Spark architecture, thereby increasing its scalability and its ability to handle large amounts of data. Experiments and comparisons with similar techniques indicate high scalability levels and high homogeneity levels in the clustering process.