Novel clustering-based pruning algorithms

Delaunay simplices pruning based clustering

In this paper, we introduce a new clustering method using the Delaunay triangulation of a set of points as an input. The proposed method is based on pruning extra simplices of a triangulation according to a local heterogeneity measure, which we introduce here. This measure produces good clustering results as it yields to better inter-cluster simplices detection. The efficiency of the measure is...

A Prior of Saliency Based Pruning Algorithms

In saliency based pruning algorithms a pruning decision is made based on an ordering of the parameters. We will in this article focus on the fact that this ordering is invariant under certain transformations, and with that knowledge an equivalence class of algorithms is developed all yielding identical prunings. A sub-class is demonstrated to have a simple but sensible interpretation in terms o...

Novel Density-Based Clustering Algorithms for Uncertain Data

Density-based techniques seem promising for handling data uncertainty in uncertain data clustering. Nevertheless, some issues have not been addressed well in existing algorithms. In this paper, we firstly propose a novel density-based uncertain data clustering algorithm, which improves upon existing algorithms from the following two aspects: (1) it employs an exact method to compute the probabi...

Novel Graph Based Clustering and Visualization Algorithms for Data Mining

A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy

Time Series Clustering is an important subroutine in many higher-level data mining analyses, including data editing for classifiers, summarization, and outlier detection. It is well known that for similarity search the superiority of Dynamic Time Warping (DTW) over Euclidean distance gradually diminishes as we consider ever larger datasets. However, as we shall show, the same is not true for cl...