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.
GriDBSCAN @cite_28 , as GF-DBSCAN, uses a regular grid, whose cells have @math -sized sides. DBSCAN is carried out in each cell, and the clusters belonging to different cells are subsequently merged if needed.
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DBSCAN is a well-known density-based data clustering algorithm that is widely used due to its ability to find arbitrarily shaped clusters in noisy data. However, DBSCAN is hard to scale which limits its utility when working with large data sets. Resilient Distributed Datasets (RDDs), on the other hand, are a fast data-processing abstraction created explicitly for in-memory computation of large data sets. This paper presents a new algorithm based on DBSCAN using the Resilient Distributed Datasets approach: RDD-DBSCAN. RDD-DBSCAN overcomes the scalability limitations of the traditional DBSCAN algorithm by operating in a fully distributed fashion. The paper also evaluates an implementation of RDD-DBSCAN using Apache Spark, the official RDD implementation. 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. Clustering analysis is a primary method for data mining. The ever increasing volumes of data in different applications forces clustering algorithms to cope with it. DBSCAN is a well-known algorithm for density-based clustering. It is both effective so it can detect arbitrary shaped clusters of dense regions and efficient especially in existence of spatial indexes to perform the neighborhood queries efficiently. In this paper we introduce a new algorithm GriDBSCAN to enhance the performance of DBSCAN using grid partitioning and merging, yielding a high performance with the advantage of high degree of parallelism. We verified the correctness of the algorithm theoretically and experimentally, studied the performance theoretically and using experiments on both real and synthetic data. It proved to run much faster than original DBSCAN. We compared the algorithm with a similar algorithm, EnhancedDBSCAN, which is also an enhancement to DBSCAN using partitioning. Experiments showed the new algorithm's superiority in performance and degree of parallelism. The last years, huge bundles of information are extracted by computational systems and electronic devices. To exploit the derived amount of data, new innovative algorithms must be employed or the established ones maybe changed. One of the most fascinating and productive techniques, in order to locate and extract information from data repositories is clustering, and DBSCAN is a successful density based algorithm which clusters data according its characteristics. However, its main disadvantage is its severe computational complexity which proves the technique very inadequate to apply on big datasets. Although DBSCAN is a very well studied technique, a fully operational parallel version of it, has not been accepted yet by the scientific community. In this work, a three phase parallel version of DBSCAN is presented. The obtained experimental results are very promising and prove the correctness, the scalability, and the effectiveness of the technique.