Density-based clustering techniques are used in a wide range of data mining applications. One of their most attractive features con- sists in not making use of prior knowledge of the number of clusters that a dataset contains along with their shape. In this paper we propose a new algorithm named Linear DBSCAN (Lin-DBSCAN), a simple approach to clustering inspired by the density model introduced with the well known algorithm DBSCAN. Designed to minimize the computational cost of density based clustering on geospatial data, Lin-DBSCAN features a linear time complexity that makes it suitable for real-time applications on low-resource devices. Lin-DBSCAN uses a discrete version of the density model of DBSCAN that takes ad- vantage of a grid-based scan and merge approach. The name of the algorithm stems exactly from its main features outlined above. The algorithm was tested with well known data sets. Experimental results prove the efficiency and the validity of this approach over DBSCAN in the context of spatial data clustering, enabling the use of a density-based clustering technique on large datasets with low computational cost.
Grouping data into meaningful clusters is an important data mining task. DBSCAN is recognized as a high quality density-based algorithm for clustering data. It enables both the determination of clusters of any shape and the identification of noise in data. The most time-consuming operation in DBSCAN is the calculation of a neighborhood for each data point. In order to speed up this operation in DBSCAN, the neighborhood calculation is expected to be supported by spatial access methods. DBSCAN, nevertheless, is not efficient in the case of high dimensional data. In this paper, we propose a new efficient TI-DBSCAN algorithm and its variant TI-DBSCAN-REF that apply the same clustering methodology as DBSCAN. Unlike DBSCAN, TI-DBSCAN and TI-DBSCAN-REF do not use spatial indices; instead they use the triangle inequality property to quickly reduce the neighborhood search space. The experimental results prove that the new algorithms are up to three orders of magnitude faster than DBSCAN, and efficiently cluster both low and high dimensional data.