Clustering in data mining is a discovery process that groups a set of data objects so that the inter-cluster similarity is minimized and intracluster similarity is maximized. In presence of noise and outlier in high dimensional data base it is a difficult task to find out the clusters of different shapes, sizes and differ in density. Density based clustering algorithms like DBSCAN finds the clu...

Software reuse is the process of implementing or updating software systems using existing software assets. Anything that is produced from a software development effort can potentially be reused. In this study, the performance of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is evaluated for Reusability Prediction of Function based Software systems. Here, the metric ba...

Knowledge discovery in large multimedia databases which usually contain large amounts of noise and high-dimensional feature vectors is an increasingly important research issue. Density-based clustering is proved to be much more efficient when dealing with such databases. However, its clustering quality mainly depends on the parameter setting. For the adequate choice of the parameters to be pres...

Cluster detection in Spatial Databases is an important task for discovery of knowledge in spatial databases and in this domain density based clustering algorithms are very effective. Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm effectively manages to detect clusters of arbitrary shape with noise, but it fails in detecting local clusters as well as clusters of d...

Spatial data mining is the task of discovering knowledge from spatial data. Density-Based Spatial Clustering occupies an important position in spatial data mining task. This paper presents a detailed survey of density-based spatial clustering of data. The various algorithms are described based on DBSCAN comparing them on the basis of various attributes and different pitfalls. The advantages and...

Existing hierarchical clustering algorithms involve a flat clustering component and an additional agglomerative or divisive procedure. This paper presents a density peak based hierarchical clustering method (DenPEHC), which directly generates clusters on each possible clustering layer, and introduces a grid granulation framework to enable DenPEHC to cluster large-scale and high-dimensional (LSH...

Doubts have been raised that time series subsequences can be clustered in a meaningful way. This paper introduces a kernel-density-based algorithm that detects meaningful patterns in the presence of a vast number of random-walk-like subsequences. The value of density-based algorithms for noise elimination in general has long been demonstrated. The challenge of applying such techniques to time-s...

In many scientific, engineering or multimedia applications, complex distance functions are used to measure similarity accurately. Furthermore, there often exist simpler lower-bounding distance functions, which can be computed much more efficiently. In this paper, we will show how these simple distance functions can be used to parallelize the density-based clustering algorithm DBSCAN. First, the...

This article describes the implementation and use of the R package dbscan, which provides complete and fast implementations of the popular density-based clustering algorithm DBSCAN and the augmented ordering algorithm OPTICS. Compared to other implementations, dbscan offers open-source implementations using C++ and advanced data structures like k-d trees to speed up computation. An important ad...