Divisive Analysis (DIANA) of hierarchical clustering and GPS data for level of service criteria of urban streets

Patnaik, Ashish Kumar; Bhuyan, Prasanta Kumar; Rao, K. V. Krishna

doi:10.1016/j.aej.2015.11.003

Cited by 32 publications

(11 citation statements)

References 18 publications

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“…The applicability analysis of CDC was performed on nine published and annotated scRNA-seq datasets, i.e., Baron-human (BH), Baron-mouse (BM) 19 , Muraro 20 , Segerstolpe 21 , Xin 22 , Allen Mouse Brain (AMB) 23 , Anterior Lateral Motor (ALM) 23 , Primary Visual Cortex (VISp) 23 and Tabula Muris (TM) 24 (see details in Supplementary Table 1 ). Seven biological baselines, i.e., Seurat v3 25 , monocle3 26 , SC3 27 , dropClust 28 , MetaCell 29 , Shared-Nearest-Neighbor-Walktrap (SNN-Walktrap) 30 , SNN-Louvain 31 , and seven versatile clustering baselines, i.e., AGNES 4 , DIANA 32 , hclust 33 , DBCSAN, K-means, C-means 34 , CLARA 4 , were selected for comparison. The standard preprocessing pipeline of scRNA-seq clustering is presented in Fig.…”

Section: Resultsmentioning

confidence: 99%

Clustering by measuring local direction centrality for data with heterogeneous density and weak connectivity

et al. 2022

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Clustering is a powerful machine learning method for discovering similar patterns according to the proximity of elements in feature space. It is widely used in computer science, bioscience, geoscience, and economics. Although the state-of-the-art partition-based and connectivity-based clustering methods have been developed, weak connectivity and heterogeneous density in data impede their effectiveness. In this work, we propose a boundary-seeking Clustering algorithm using the local Direction Centrality (CDC). It adopts a density-independent metric based on the distribution of K-nearest neighbors (KNNs) to distinguish between internal and boundary points. The boundary points generate enclosed cages to bind the connections of internal points, thereby preventing cross-cluster connections and separating weakly-connected clusters. We demonstrate the validity of CDC by detecting complex structured clusters in challenging synthetic datasets, identifying cell types from single-cell RNA sequencing (scRNA-seq) and mass cytometry (CyTOF) data, recognizing speakers on voice corpuses, and testifying on various types of real-world benchmarks.

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Section: Resultsmentioning

confidence: 99%

Clustering by measuring local direction centrality for data with heterogeneous density and weak connectivity

et al. 2022

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“…From the first group of algorithms, we can find numerous examples in the literature [12,13,14]. However, in this work we present a version of the agglomerative option.…”

Section: Clustering Methodsmentioning

confidence: 99%

Indexes to Find the Optimal Number of Clusters in a Hierarchical Clustering

Martín-Fernández

Luna-Romera

Pontes

et al. 2019

Advances in Intelligent Systems and Computing

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Clustering analysis is one of the most commonly used techniques for uncovering patterns in data mining. Most clustering methods require establishing the number of clusters beforehand. However, due to the size of the data currently used, predicting that value is at a high computational cost task in most cases. In this article, we present a clustering technique that avoids this requirement, using hierarchical clustering. There are many examples of this procedure in the literature, most of them focusing on the dissociative or descending subtype, while in this article we cover the agglomerative or ascending subtype. Being more expensive in computational and temporal cost, it nevertheless allows us to obtain very valuable information, regarding elements membership to clusters and their groupings, that is to say, their dendrogram. Finally, several sets of data have been used, varying their dimensionality. For each of them, we provide the calculations of internal validation indexes to test the algorithm developed, studying which of them provides better results to obtain the best possible clustering.

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“…DIANA is a technique that constructs the hierarchy in the inverse order [27]. First, all the data points are considered as a single cluster.…”

Section: ) Hierarchical Clusteringmentioning

confidence: 99%

The Performance of Spectral Clustering Algorithms on Water Distribution Networks: Further Evidence

Belloum

Houichi

Kherouf

2022

Eng. Technol. Appl. Sci. Res.

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The aim of the current paper is to revisit the performance of spectral clustering algorithms for water distribution networks. In the literature, there have been attempts to introduce improved algorithms based on graph theory. We focus on a class of these algorithms that applies the concepts of the spectral clustering approach. We assess the performance of spectral clustering algorithms on a wider range of water network types (i.e. large, medium, and small sized networks) using a wider range of clustering methods (both partitioning and hierarchical) and performance indicators. Our findings suggest that partitioning methods, such as k-means are not consistently efficient in all types of networks. Nonetheless, the Partitioning Around Medoids (PAM) algorithm shows a relatively good performance according to modularity, while the internal indices of k-means and hierarchical clustering algorithms are more efficient. Stability indices show that PAM and CLARA algorithms are more efficient.

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Divisive Analysis (DIANA) of hierarchical clustering and GPS data for level of service criteria of urban streets

Cited by 32 publications

References 18 publications

Clustering by measuring local direction centrality for data with heterogeneous density and weak connectivity

Clustering by measuring local direction centrality for data with heterogeneous density and weak connectivity

Indexes to Find the Optimal Number of Clusters in a Hierarchical Clustering

The Performance of Spectral Clustering Algorithms on Water Distribution Networks: Further Evidence

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