A distance-type-insensitive clustering approach

Abstract

In this paper, we offer a method aiming to minimize the role of distance metric used in clustering. It is well known that distance metrics used in clustering algorithms heavily influence the end results and also make the algorithms sensitive to imbalanced attribute/feature scales. To solve these problems, a new clustering algorithm using a per-attribute/feature ranking operating mechanism is proposed in this paper. Ranking is a rarely used discrete, nonlinear operator by other clustering algorithms. However, it also has unique advantages over the dominantly used continuous operators. The proposed algorithm is based on the ranks of the data samples in terms of their spatial separation and is able to provide a more objective clustering result compared with the alternative approaches. Numerical examples on benchmark datasets prove the validity and effectiveness of the proposed concept and principles.

Hierarchical Phenotyping and Graph Modeling of Spatial Architecture in Lymphoid Neoplasms

“…In the local cell graph construction, the cell centroids together with the obtained cell subtypes information are used to identify cell clusters {C}, where the Ranking Operation-based Clustering (ROC) algorithm applied (detailed in Fig. 2) [8].…”

Incremental Instance-Oriented 3D Semantic Mapping via RGB-D Cameras for Unknown Indoor Scene

“…In order to obtain more accurate object representation, we optimized the objects by formulating an accelerated generative model in the form of a GMM with a highly parallel hierarchical expectation-maximization (EM) algorithm, inspired by [39]. Also, there is an alternative clustering approach which can be used for optimization, such as ROC algorithm [12]. As a cluster solution for 3D point cloud data, the advantages of GMM are suited to our work.…”

“…e latter usually carries out simultaneous localization and mapping (SLAM) [7][8][9], which completes 3D scene reconstruction using RGB-D cameras. Motivated by the mentioned technologies, several works efficiently combine them to generate a semantically segmented 3D map [10][11][12] and have achieved impressive results. However, such methods suffer from the oversegment problem or lack of proper data association strategy, and meanwhile, they are computationally inefficient, making them unsuitable for the real-time applications.…”