Efficient Hierarchical Agglomerative Clustering Algorithms on GPU Using Data Partitioning

2011 ◽  
Author(s):  
S.A. Arul Shalom ◽  
Manoranjan Dash
Keyword(s):  
Clustering Algorithms ◽  
Data Partitioning ◽  
Agglomerative Clustering ◽  
Hierarchical Agglomerative Clustering ◽  
Using Data

scholarly journals Investigating homicide offender typologies based on their clinical histories and crime scene behaviour patterns

2019 ◽  
Vol 5 (3) ◽  
pp. 168-188
Author(s):  
Valeria Abreu ◽  
Edward Barker ◽  
Hannah Dickson ◽  
Francois Husson ◽  
Sandra Flynn ◽  
...  
Keyword(s):  
Mental Health ◽  
Multiple Correspondence Analysis ◽  
Empirical Studies ◽  
Crime Scene ◽  
Agglomerative Clustering ◽  
Content Type ◽  
Offender Profiling ◽  
Hierarchical Agglomerative Clustering ◽  
Using Data ◽  
Offender Typologies

Purpose The purpose of this paper is to identify offender typologies based on aspects of the offenders’ psychopathology and their associations with crime scene behaviours using data derived from the National Confidential Enquiry into Suicide and Safety in Mental Health concerning homicides in England and Wales committed by offenders in contact with mental health services in the year preceding the offence (n=759). Design/methodology/approach The authors used multiple correspondence analysis to investigate the interrelationships between the variables and hierarchical agglomerative clustering to identify offender typologies. Variables describing: the offenders’ mental health histories; the offenders’ mental state at the time of offence; characteristics useful for police investigations; and patterns of crime scene behaviours were included. Findings Results showed differences in the offenders’ histories in relation to their crime scene behaviours. Further, analyses revealed three homicide typologies: externalising, psychosis and depression. Practical implications These typologies may assist the police during homicide investigations by: furthering their understanding of the crime or likely suspect; offering insights into crime patterns; provide advice as to what an offender’s offence behaviour might signify about his/her mental health background. Findings suggest information concerning offender psychopathology may be useful for offender profiling purposes in cases of homicide offenders with schizophrenia, depression and comorbid diagnosis of personality disorder and alcohol/drug dependence. Originality/value Empirical studies with an emphasis on offender profiling have almost exclusively focussed on the inference of offender demographic characteristics. This study provides a first step in the exploration of offender psychopathology and its integration to the multivariate analysis of offence information for the purposes of investigative profiling of homicide by identifying the dominant patterns of mental illness within homicidal behaviour.

scholarly journals Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 267
Author(s):  
Félix Morales ◽  
Miguel García-Torres ◽  
Gustavo Velázquez ◽  
Federico Daumas-Ladouce ◽  
Pedro E. Gardel-Sotomayor ◽  
...  
Keyword(s):  
Clustering Algorithms ◽  
Electric Energy ◽  
Real Data ◽  
Original Data ◽  
Data Sets ◽  
Agglomerative Clustering ◽  
Daily Consumption ◽  
Load Curve ◽  
Electric Energy Consumption ◽  
Hierarchical Agglomerative Clustering

Correctly defining and grouping electrical feeders is of great importance for electrical system operators. In this paper, we compare two different clustering techniques, K-means and hierarchical agglomerative clustering, applied to real data from the east region of Paraguay. The raw data were pre-processed, resulting in four data sets, namely, (i) a weekly feeder demand, (ii) a monthly feeder demand, (iii) a statistical feature set extracted from the original data and (iv) a seasonal and daily consumption feature set obtained considering the characteristics of the Paraguayan load curve. Considering the four data sets, two clustering algorithms, two distance metrics and five linkage criteria a total of 36 models with the Silhouette, Davies–Bouldin and Calinski–Harabasz index scores was assessed. The K-means algorithms with the seasonal feature data sets showed the best performance considering the Silhouette, Calinski–Harabasz and Davies–Bouldin validation index scores with a configuration of six clusters.

scholarly journals Application of Hierarchical Agglomerative Clustering (HAC) for Systemic Classification of Pop-Up Housing (PUH) Environments

2021 ◽  
Vol 11 (23) ◽  
pp. 11122
Author(s):  
Thomas Märzinger ◽  
Jan Kotík ◽  
Christoph Pfeifer
Keyword(s):  
Urban Planning ◽  
Hierarchical Clustering ◽  
Clustering Algorithms ◽  
Field Studies ◽  
Assessment Tools ◽  
Agglomerative Clustering ◽  
Research Project ◽  
Minkowski Metric ◽  
Hierarchical Agglomerative Clustering

This paper is the result of the first-phase, inter-disciplinary work of a multi-disciplinary research project (“Urban pop-up housing environments and their potential as local innovation systems”) consisting of energy engineers and waste managers, landscape architects and spatial planners, innovation researchers and technology assessors. The project is aiming at globally analyzing and describing existing pop-up housings (PUH), developing modeling and assessment tools for sustainable, energy-efficient and socially innovative temporary housing solutions (THS), especially for sustainable and resilient urban structures. The present paper presents an effective application of hierarchical agglomerative clustering (HAC) for analyses of large datasets typically derived from field studies. As can be shown, the method, although well-known and successfully established in (soft) computing science, can also be used very constructively as a potential urban planning tool. The main aim of the underlying multi-disciplinary research project was to deeply analyze and structure THS and PUE. Multiple aspects are to be considered when it comes to the characterization and classification of such environments. A thorough (global) web survey of PUH and analysis of scientific literature concerning descriptive work of PUH and THS has been performed. Moreover, out of several tested different approaches and methods for classifying PUH, hierarchical clustering algorithms functioned well when properly selected metrics and cut-off criteria were applied. To be specific, the ‘Minkowski’-metric and the ‘Calinski-Harabasz’-criteria, as clustering indices, have shown the best overall results in clustering the inhomogeneous data concerning PUH. Several additional algorithms/functions derived from the field of hierarchical clustering have also been tested to exploit their potential in interpreting and graphically analyzing particular structures and dependencies in the resulting clusters. Hereby, (math.) the significance ‘S’ and (math.) proportion ‘P’ have been concluded to yield the best interpretable and comprehensible results when it comes to analyzing the given set (objects n = 85) of researched PUH-objects together with their properties (n > 190). The resulting easily readable graphs clearly demonstrate the applicability and usability of hierarchical clustering- and their derivative algorithms for scientifically profound building classification tasks in Urban Planning by effectively managing huge inhomogeneous building datasets.

Clustering of Micro-Messages Using Similarity Upper Approximation

2017 ◽  
Vol 25 (01) ◽  
pp. 53-79 ◽  
Author(s):  
Mukul Gupta ◽  
Pradeep Kumar ◽  
Bharat Bhasker
Keyword(s):  
Clustering Algorithm ◽  
State Of The Art ◽  
Clustering Algorithms ◽  
Mining Operation ◽  
Agglomerative Clustering ◽  
Upper Approximation ◽  
Affinity Propagation Clustering ◽  
Hierarchical Agglomerative Clustering ◽  
Text Content ◽  
Extract Information

Microblogging platforms like Twitter, Tumblr and Plurk have radically changed our lives. The presence of millions of people has made these platforms a preferred channel for communication. A large amount of User Generated Content, on these platforms, has attracted researchers and practitioners to mine and extract information nuggets. For information extraction, clustering is an important and widely used mining operation. This paper addresses the issue of clustering of micro-messages and corresponding users based on the text content of micro-messages that reflect their primitive interest. In this paper, we performed modification of the Similarity Upper Approximation based clustering algorithm for clustering of micro-messages. We compared the performance of the modified Similarity Upper Approximation based clustering algorithm with state-of-the-art clustering algorithms such as Partition Around Medoids, Hierarchical Agglomerative Clustering, Affinity Propagation Clustering and DBSCAN. Experiments were performed on micro-messages collected from Twitter. Experimental results show the effectiveness of the proposed algorithm.

scholarly journals Clustering of a Health Dataset Using Diagnosis Co-Occurrences

2021 ◽  
Vol 11 (5) ◽  
pp. 2373
Author(s):  
Adrien Wartelle ◽  
Farah Mourad-Chehade ◽  
Farouk Yalaoui ◽  
Jan Chrusciel ◽  
David Laplanche ◽  
...  
Keyword(s):  
Quality Indicators ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Agglomerative Clustering ◽  
Cluster Membership ◽  
Healthcare Needs ◽  
Similar Patient ◽  
Hierarchical Agglomerative Clustering ◽  
Patient Profiles ◽  
Cluster Compactness

Assessing the health profiles of populations is a crucial task to create a coherent healthcare offer. Emergency Departments (EDs) are at the core of the healthcare system and could benefit from this evaluation via an improved understanding of the healthcare needs of their population. This paper proposes a novel hierarchical agglomerative clustering algorithm based on multimorbidity analysis. The proposed approach constructs the clustering dendrogram by introducing new quality indicators based on the relative risk of co-occurrences of patient diagnoses. This algorithm enables the detection of multimorbidity patterns by merging similar patient profiles according to their common diagnoses. The multimorbidity approach has been applied to the data of the largest ED of the Aube Department (Eastern France) to cluster its patient visits. Among the 120,718 visits identified during a 24-month period, 16 clusters were identified, accounting for 94.8% of the visits, with the five most prevalent clusters representing 63.0% of them. The new quality indicators show a coherent and good clustering solution with a cluster membership of 1.81 based on a cluster compactness of 1.40 and a cluster separation of 0.77. Compared to the literature, the proposed approach is appropriate for the discovery of multimorbidity patterns and could help to develop better clustering algorithms for more diverse healthcare datasets.

scholarly journals A Hierarchical Clustering Algorithm Based on Silhouette Index for Cancer Subtype Discovery from Omics Data

2018 ◽  
Author(s):  
N. Nidheesh ◽  
K.A. Abdul Nazeer ◽  
P.M. Ameer
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Agglomerative Clustering ◽  
Cluster Validity ◽  
Cluster Validity Index ◽  
Number Of Clusters ◽  
Silhouette Index ◽  
Cancer Subtype ◽  
Hierarchical Agglomerative Clustering ◽  
Hierarchical Clustering Algorithm

AbstractCancer subtype discovery fromomicsdata requires techniques to estimate the number of natural clusters in the data. Automatically estimating the number of clusters has been a challenging problem in Machine Learning. Using clustering algorithms together with internal cluster validity indexes have been a popular method of estimating the number of clusters in biomolecular data. We propose a Hierarchical Agglomerative Clustering algorithm, namedSilHAC, which can automatically estimate the number of natural clusters and can find the associated clustering solution.SilHACis parameterless. We also present two hybrids ofSilHACwithSpectral ClusteringandK-Meansrespectively as components.SilHACand the hybrids could find reasonable estimates for the number of clusters and the associated clustering solution when applied to a collection of cancer gene expression datasets. The proposed methods are better alternatives to the ‘clustering algorithm - internal cluster validity index’ pipelines for estimating the number of natural clusters.

scholarly journals Robustness of fish assemblages derived from three hierarchical agglomerative clustering algorithms performed on Icelandic groundfish survey data

2010 ◽  
Vol 68 (1) ◽  
pp. 189-200 ◽  
Author(s):  
Warsha Singh ◽  
Einar Hjorleifsson ◽  
Gunnar Stefansson
Keyword(s):  
Survey Data ◽  
Fish Assemblages ◽  
Clustering Algorithms ◽  
Agglomerative Clustering ◽  
Cluster Validity Indices ◽  
Validity Indices ◽  
Hierarchical Agglomerative Clustering ◽  
Species Area ◽  
Species Groups ◽  
The Stability

Abstract Singh, W., Hjorleifsson, E., and Stefansson, G. 2011. Robustness of fish assemblages derived from three hierarchical agglomerative clustering algorithms performed on Icelandic groundfish survey data. – ICES Journal of Marine Science, 68: 189–200. Heatmaps are used to identify species–area assemblages based on Icelandic groundfish survey data. Hierarchical agglomerative clustering algorithms are widely applied for species assemblage studies and form the basis for heatmaps. First, the robustness of fish assemblages derived from three clustering algorithms, Average, Complete, and Ward's linkage, was examined. For statistical reliability, the use of a bootstrap resampling technique to generate the confidence values for the clusters is emphasized. Two cluster validity indices were used to measure the efficiency and the quality of the clusters. To examine the stability of the results, clustering was carried out across different sample sizes and levels of data smoothing. Second, cluster analysis was carried out using a different combination of data standardization and dissimilarity measure. Ward's linkage gave the most robust fish assemblages for both modes of data analyses. Four fish assemblages were identified which could be characterized according to the depth and the geographic distribution. This algorithm was then used to generate a heatmap to determine the species–area relationships. Specific areas were characterized by the identified species groups.

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