G-DBSCAN: An Improved DBSCAN Clustering Method Based On Grid

Abstract

Clustering is one of the most active research fields in data mining. Clustering in statistics, pattern recognition, image processing, machine learning, biology, marketing and many other fields have a wide range of applications. DBSCAN is a density-based clustering algorithm. this algorithm clusters data of high density. The traditional DBSCAN clustering algorithm in finding the core object, will use this object as the center core, extends outwards continuously. At this point, the core objects growing, unprocessed objects are retained in memory, which will occupy a lot of memory and I/O overhead, algorithm efficiency is not high. In order to ensure the high efficiency of DBSCAN clustering algorithm, and reduce its memory footprint. In this paper, the original DBSCAN algorithm was improved, and the G-DBSCAN algorithm is proposed. G-DBSCAN algorithm to reduce the number of query object as a starting point, Put the data into the grid, with the center point of the data in the grid to replace all the grid points as the algorithm input. The query object will be drastically reduced, thus improving the efficiency of the algorithm, reduces the memory footprint. The results prove that G-DBSCAN algorithm is feasible and effective.

MCARS-CC: A Scalable Multi-context-aware Recommender System

“…The DBSCAN (Density Based Spatial Clustering of Applications with Noise) algorithm is commonly utilized in the density-based clustering approach. DBSCAN operates using parameters like (MinPts, eps, core points, and ε) [112]. DBSCAN works by defining dense regions of connected points, where those regions are detached by other lower-dense regions (sparse) [113].…”

The GridOPTICS clustering algorithm

“…G-DBSCAN (Ma et al, 2014) uses a grid method for the first time, and removes noise in order to reduce the points to be processed. Its goal was to reduce memory usage and improve efficiency of the algorithm.…”