Clustering Via Nonparametric Density Estimation: the R Package pdfCluster

The R package pdfCluster performs cluster analysis based on a nonparametric estimate of the density of the observed variables. After summarizing the main aspects of the methodology, we describe the features and the usage of the package, and finally illustrate its working with the aid of two datasets

Adaptive gravitational softening in GADGET

Cosmological simulations of structure formation follow the collisionless evolution of dark matter starting from a nearly homogeneous field at early times down to the highly clustered configuration at redshift zero. The density field is sampled by a number of particles in number infinitely smaller than those believed to be its actual components and this limits the mass and spatial scales over which we can trust the results of a simulation. Softening of the gravitational force is introduced in collisionless simulations to limit the importance of close encounters between these particles. The scale of softening is generally fixed and chosen as a compromise between the need for high spatial resolution and the need to limit the particle noise. In the scenario of cosmological simulations, where the density field evolves to a highly inhomogeneous state, this compromise results in an appropriate choice only for a certain class of objects, the others being subject to either a biased or a noisy dynamical description. We have implemented adaptive gravitational softening lengths in the cosmological simulation code GADGET; the formalism allows the softening scale to vary in space and time according to the density of the environment, at the price of modifying the equation of motion for the particles in order to be consistent with the new dependencies introduced in the system's Lagrangian. We have applied the technique to a number of test cases and to a set of cosmological simulations of structure formation. We conclude that the use of adaptive softening enhances the clustering of particles at small scales, a result visible in the amplitude of the correlation function and in the inner profile of massive objects, thereby anticipating the results expected from much higher resolution simulations.Comment: 15 pages, 21 figures, 1 table. Accepted for publication in MNRA

Finding and tracking multi-density clusters in an online dynamic data stream

The file attached to this record is the author's final peer reviewed version.Change is one of the biggest challenges in dynamic stream mining. From a data-mining perspective, adapting and tracking change is desirable in order to understand how and why change has occurred. Clustering, a form of unsupervised learning, can be used to identify the underlying patterns in a stream. Density-based clustering identifies clusters as areas of high density separated by areas of low density. This paper proposes a Multi-Density Stream Clustering (MDSC) algorithm to address these two problems; the multi-density problem and the problem of discovering and tracking changes in a dynamic stream. MDSC consists of two on-line components; discovered, labelled clusters and an outlier buffer. Incoming points are assigned to a live cluster or passed to the outlier buffer. New clusters are discovered in the buffer using an ant-inspired swarm intelligence approach. The newly discovered cluster is uniquely labelled and added to the set of live clusters. Processed data is subject to an ageing function and will disappear when it is no longer relevant. MDSC is shown to perform favourably to state-of-the-art peer stream-clustering algorithms on a range of real and synthetic data-streams. Experimental results suggest that MDSC can discover qualitatively useful patterns while being scalable and robust to noise