Open AccessDensity Based Spatial Clustering Application with Noise by Varying Densities
Author(s) -
Vikram Neerugatti,
Mohammad Ali Moni,
Rama Mohan Reddy A
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.d8757.118419
Subject(s) - dbscan , cluster analysis , cluster (spacecraft) , noise (video) , computer science , algorithm , pattern recognition (psychology) , value (mathematics) , artificial intelligence , data mining , cure data clustering algorithm , correlation clustering , machine learning , image (mathematics) , programming language
Cluster algorithms are used for grouping up of similar points to form a cluster. It has seen mostly in Machine Learning algorithms. The most popular density-based algorithm is DBSCAN. DBSCAN can find the clusters, irrespective of its shapes and sizes of a cluster. DBSCAN algorithm can easily detect the noise in a clustering dataset. In the proposed algorithm we developed a model based on the existing dbscan algorithm. In the developed algorithm we focus mainly on the epsilon parameter value. Whenever the dbscan algorithm fails to form a cluster we increase the epsilon value by half of its original size. We repeat this step until a cluster is formed. Whenever a cluster is newly formed we change existing epsilon parameter value by adding the 10 percent of the previous used epsilon parameter value. We use epsilon for varying the density of a cluster. So, we can use the dbscan algorithm with the varying density values for developing a cluster. We applied this algorithm on the various datasets.