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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Resilient Node Cluster Analysis Tool (ReNCAT) v. 2.1

SAND2021-6533 O The Resilient Node Cluster Analysis Tool (ReNCAT) identifies portions of a power distribution network that can be isolated into microgrids in order to supply services to a community during disruptive events. Candidate microgrid configurations are evaluated using a burden metric, a measure of the relative effort community members must expend to acquire services required for their well-being. Results present multiple candidate configurations, each with a different balance of cost and burden. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Melander, Darryl↗
NASA NTRS1973

The applicability and effectiveness of cluster analysis

An insight into the characteristics which determine the performance of a clustering algorithm is presented. In order for the techniques which are examined to accurately cluster data, two conditions must be simultaneously satisfied. First the data must have a particular structure, and second the parameters chosen for the clustering algorithm must be correct. By examining the structure of the data from the Cl flight line, it is clear that no single set of parameters can be used to accurately cluster all the different crops. The effectiveness of either a noniterative or iterative clustering algorithm to accurately cluster data representative of the Cl flight line is questionable. Thus extensive a prior knowledge is required in order to use cluster analysis in its present form for applications like assisting in the definition of field boundaries and evaluating the homogeneity of a field. New or modified techniques are necessary for clustering to be a reliable tool.

Ingram, D. S.↗
NASA NTRS1975

A method of using cluster analysis to study statistical dependence in multivariate data

A technique is presented that uses both cluster analysis and a Monte Carlo significance test of clusters to discover associations between variables in multidimensional data. The method is applied to an example of a noisy function in three-dimensional space, to a sample from a mixture of three bivariate normal distributions, and to the well-known Fisher's Iris data.

Borucki, W. J.↗
NASA NTRS1992

The use of the wavelet cluster analysis for asteroid family determination

The asteroid family determination has been analysis method dependent for a longtime. A new cluster analysis based on the wavelet transform has allowed an automatic definition of families with a degree of significance versus randomness. Actually this method is rather general and can be applied to any kind of structural analysis. We will rather concentrate on the main features of the method. The analysis has been performed on the set of 4100 asteroid proper elements computed by Milani and Knezevic (see Milani and Knezevic 1990). Twenty one families have been found and influence of the chosen metric has been tested. The results have beem compared to Zappala et al.'s ones (see Zappala et al 1990) obtained by the use of a completely different method applied to the same set of data. For the first time, a good overlapping has been found between both method results, not only for the big well known families but also for the smallest ones.

Benjoya, Phillippe↗
NASA NTRS2003

Archetypal TRMM Radar Profiles Identified Through Cluster Analysis

It is widely held that identifiable 'convective regimes' exist in nature, although precise definitions of these are elusive. Examples include land / Ocean distinctions, break / monsoon beahvior, seasonal differences in the Amazon (SON vs DJF), etc. These regimes are often described by differences in the realized local convective spectra, and measured by various metrics of convective intensity, depth, areal coverage and rainfall amount. Objective regime identification may be valuable in several ways: regimes may serve as natural 'branch points' in satellite retrieval algorithms or data assimilation efforts; one example might be objective identification of regions that 'should' share a similar 2-R relationship. Similarly, objectively defined regimes may provide guidance on optimal siting of ground validation efforts. Objectively defined regimes could also serve as natural (rather than arbitrary geographic) domain 'controls' in studies of convective response to environmental forcing. Quantification of convective vertical structure has traditionally involved parametric study of prescribed quantities thought to be important to convective dynamics: maximum radar reflectivity, cloud top height, 30-35 dBZ echo top height, rain rate, etc. Individually, these parameters are somewhat deficient as their interpretation is often nonunique (the same metric value may signify different physics in different storm realizations). Individual metrics also fail to capture the coherence and interrelationships between vertical levels available in full 3-D radar datasets. An alternative approach is discovery of natural partitions of vertical structure in a globally representative dataset, or 'archetypal' reflectivity profiles. In this study, this is accomplished through cluster analysis of a very large sample (0[107) of TRMM-PR reflectivity columns. Once achieved, the rainconditional and unconditional 'mix' of archetypal profile types in a given location and/or season provides a description of the local convective spectrum which retains vertical structure information. A further cluster analysis of these 'mixes' can identify recurrent convective spectra. These are a first step towards objective identification of convective regimes, and towards answering the question: 'What are the most convectively similar locations in the world?'

Boccippio, Dennis J.↗
DOE OSTI2016

Robustness of Dynamical Cluster Analysis in a Glass-Forming Metallic Liquid using an Unsupervised Machine Learning Algorithm

We performed dynamical cluster analysis in a Cu-Zr-Al based glass-forming metallic liquid using an unsupervised machine learning algorithm. The size of the dynamical clusters is used to quantify the onset of cooperative dynamics as the underlying mechanism leading to the Arrhenius dynamic crossover in transport coefficients of the metallic liquid. This technique is useful to directly visualize dynamical clusters and quantify their sizes upon cooling. Here, we demonstrate the robustness of this algorithm by performing sensitivity analysis against two key parameters: number of mobility groups and inconsistency coefficient of the hierarchical cluster tree. The results elucidate the optimized range of values for both of these parameters that capture the underlying physical picture of increasing cooperative dynamics appropriately.

36 MATERIALS SCIENCE↗
DOE OSTI2016

EVOLUTION OF STELLAR-TO-HALO MASS RATIO AT z = 0–7 IDENTIFIED BY CLUSTERING ANALYSIS WITH THE HUBBLE LEGACY IMAGING AND EARLY SUBARU/HYPER SUPRIME-CAM SURVEY DATA

We present clustering analysis results from 10,381 Lyman break galaxies (LBGs) at z ∼ 4–7, identified in the Hubble legacy deep imaging and new complimentary large-area Subaru/Hyper Suprime-Cam data. We measure the angular correlation functions of these LBGs at z ∼ 4, 5, 6, and 7 and fit these measurements using halo occupation distribution (HOD) models that provide an estimate of halo masses, M{sub h}∼(1−20)×10{sup 11} M{sub ⊙}. Our M{sub h} estimates agree with those obtained by previous clustering studies in a UV-magnitude versus M{sub h} plane and allow us to calculate stellar-to-halo mass ratios (SHMRs) of LBGs. By comparison with the z∼0 SHMR, we identify evolution of the SHMR from z∼0 to z∼4 and from z∼4 to z∼7 at the >98% confidence levels. The SHMR decreases by a factor of ∼2 from z∼0 to 4 and increases by a factor of ∼4 from z∼4 to 7 at the dark matter halo mass of M{sub h}∼10{sup 11} M{sub ⊙}. We compare our SHMRs with results of a hydrodynamic simulation and a semianalytic model and find that these theoretical studies do not predict the SHMR increase from z∼4 to 7. We obtain the baryon conversion efficiency (BCE) of LBGs at z∼4 and find that the BCE increases with increasing dark matter halo mass. Finally, we compare our clustering+HOD estimates with results from abundance matching techniques and conclude that the M{sub h} estimates of the clustering+HOD analyses agree with those of the simple abundance matching within a factor of 3, and that the agreement improves when using more sophisticated abundance matching techniques that include subhalos, incompleteness, and/or evolution in the star formation and stellar mass functions.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1982

An application of cluster analysis for determining homogeneous subregions: The agroclimatological point of view

A stratification oriented to crop area and yield estimation problems was performed using an algorithm of clustering. The variables used were a set of agroclimatological characteristics measured in each one of the 232 municipalities of the State of Rio Grande do Sul, Brazil. A nonhierarchical cluster analysis was used and the pseudo F-statistics criterion was implemented for determining the "cut point" in the number of strata.

Parada, N. D. J.↗
DOE OSTI2017

Cluster analysis of accelerated molecular dynamics simulations: A case study of the decahedron to icosahedron transition in Pt nanoparticles

Modern molecular-dynamics-based techniques are extremely powerful to investigate the dynamical evolution of materials. With the increase in sophistication of the simulation techniques and the ubiquity of massively parallel computing platforms, atomistic simulations now generate very large amounts of data, which has to be carefully analyzed in order to reveal key features of the underlying trajectories, including the nature and characteristics of the relevant reaction pathways. We show that clustering algorithms, such as the Perron Cluster Cluster Analysis, can provide reduced representations that greatly facilitate the interpretation of complex trajectories. To illustrate this point, clustering tools are used to identify the key kinetic steps in complex Accelerated Molecular Dynamics trajectories exhibiting shape uctuations in Pt nanoclusters. This analysis provides an easily-interpretable coarse representation of the reaction pathways in terms of a handful of clusters, in contrast to the raw trajectory that contains thousands of unique states and tens of thousands of transitions.

74 ATOMIC AND MOLECULAR PHYSICS↗
NASA NTRS1973

Editing ERTS-1 data to exclude land aids cluster analysis of water targets

The author has identified the following significant results. It has been determined that an increase in the number of spectrally distinct coastal water types is achieved when data values over the adjacent land areas are excluded from the processing routine. This finding resulted from an automatic clustering analysis of ERTS-1 system corrected MSS scene 1002-18134 of 25 July 1972 over Monterey Bay, California. When the entire study area data set was submitted to the clustering only two distinct water classes were extracted. However, when the land area data points were removed from the data set and resubmitted to the clustering routine, four distinct groupings of water features were identified. Additionally, unlike the previous separation, the four types could be correlated to features observable in the associated ERTS-1 imagery. This exercise demonstrates that by proper selection of data submitted to the processing routine, based upon the specific application of study, additional information may be extracted from the ERTS-1 MSS data.

Erb, R. B.↗
DOE OSTI2023

Subseasonal Representation and Predictability of North American Weather Regimes Using Cluster Analysis

Abstract This study focuses on assessing the representation and predictability of North American weather regimes, which are persistent large-scale atmospheric patterns, in a set of initialized subseasonal reforecasts created using the Community Earth System Model, version 2 (CESM2). The k -means clustering was used to extract four key North American (10°–70°N, 150°–40°W) weather regimes within ERA5 reanalysis, which were used to interpret CESM2 subseasonal forecast performance. Results show that CESM2 can recreate the climatology of the four main North American weather regimes with skill but exhibits biases during later lead times with overoccurrence of the West Coast high regime and underoccurrence of the Greenland high and Alaskan ridge regimes. Overall, the West Coast high and Pacific trough regimes exhibited higher predictability within CESM2, partly related to El Niño. Despite biases, several reforecasts were skillful and exhibited high predictability during later lead times, which could be partly attributed to skillful representation of the atmosphere from the tropics to extratropics upstream of North America. The high predictability at the subseasonal time scale of these case-study examples was manifested as an “ensemble realignment,” in which most ensemble members agreed on a prediction despite ensemble trajectory dispersion during earlier lead times. Weather regimes were also shown to project distinct temperature and precipitation anomalies across North America that largely agree with observational products. This study further demonstrates that unsupervised learning methods can be used to uncover sources and limits of subseasonal predictability, along with systematic biases present in numerical prediction systems. Significance Statement North American weather regimes are large-scale atmospheric patterns that can persist for several days. Their skillful subseasonal (2 weeks or greater) prediction can provide valuable lead time to prepare for temperature and precipitation anomalies that can stress energy and water resources. The purpose of this study was to assess the climatological representation and subseasonal predictability of four key North American weather regimes using a research subseasonal prediction system and clustering analysis. We found that the Pacific trough and West Coast high regimes exhibited higher predictability than other regimes and that skillful representation of conditions across the tropics and extratropics can increase predictability during later lead times. Future work will quantify causal pathways associated with high predictability.

58 GEOSCIENCES↗
DOE OSTI2026

Clustering analysis of medium-band selected high-redshift galaxies

Next-generation large-scale structure spectroscopic surveys will probe cosmology at high redshifts (2.3 < z < 3.5), relying on abundant galaxy tracers such as Lyα emitters (LAEs) and Lyman break galaxies (LBGs). Medium-band photometry has emerged as a potential technique for efficiently selecting these high-redshift galaxies. In this work, we present clustering analysis of medium-band selected galaxies at high redshift, utilizing photometric data from the Intermediate Band Imaging Survey (IBIS) and spectroscopic data from the Dark Energy Spectroscopic Instrument (DESI). We interpret the clustering of such samples using both Halo Occupation Distribution (HOD) modeling and a perturbation theory description of large-scale structure. Our modeling indicates that the current target sample is composed from an overlapping mixture of LAEs and LBGs with emission lines. Despite differences in target selection, we find that the clustering properties are consistent with previous studies, with correlation lengths r 0 ≃ 3-4 h -1 Mpc and a linear bias of b ∼ 1.8-2.5. Finally, we discuss the simulation requirements implied by these measurements and demonstrate that the properties of the samples would make them excellent targets to enhance our understanding of the high-z universe.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1993

Multi-viewpoint clustering analysis

In this paper, we address the feasibility of partitioning rule-based systems into a number of meaningful units to enhance the comprehensibility, maintainability and reliability of expert systems software. Preliminary results have shown that no single structuring principle or abstraction hierarchy is sufficient to understand complex knowledge bases. We therefore propose the Multi View Point - Clustering Analysis (MVP-CA) methodology to provide multiple views of the same expert system. We present the results of using this approach to partition a deployed knowledge-based system that navigates the Space Shuttle's entry. We also discuss the impact of this approach on verification and validation of knowledge-based systems.

Mehrotra, Mala↗
NASA NTRS1993

The methodology of multi-viewpoint clustering analysis

One of the greatest challenges facing the software engineering community is the ability to produce large and complex computer systems, such as ground support systems for unmanned scientific missions, that are reliable and cost effective. In order to build and maintain these systems, it is important that the knowledge in the system be suitably abstracted, structured, and otherwise clustered in a manner which facilitates its understanding, manipulation, testing, and utilization. Development of complex mission-critical systems will require the ability to abstract overall concepts in the system at various levels of detail and to consider the system from different points of view. Multi-ViewPoint - Clustering Analysis MVP-CA methodology has been developed to provide multiple views of large, complicated systems. MVP-CA provides an ability to discover significant structures by providing an automated mechanism to structure both hierarchically (from detail to abstract) and orthogonally (from different perspectives). We propose to integrate MVP/CA into an overall software engineering life cycle to support the development and evolution of complex mission critical systems.

Mehrotra, Mala↗
NASA NTRS1985

A cluster analysis method for identification of subpopulations of cells in flow cytometric list-mode arrays

A specialized program was developed for flow cytometric list-mode data using an heirarchical tree method for identifying and enumerating individual subpopulations, the method of principal components for a two-dimensional display of 6-parameter data array, and a standard sorting algorithm for characterizing subpopulations. The program was tested against a published data set subjected to cluster analysis and experimental data sets from controlled flow cytometry experiments using a Coulter Electronics EPICS V Cell Sorter. A version of the program in compiled BASIC is usable on a 16-bit microcomputer with the MS-DOS operating system. It is specialized for 6 parameters and up to 20,000 cells. Its two-dimensional display of Euclidean distances reveals clusters clearly, as does its 1-dimensional display. The identified subpopulations can, in suitable experiments, be related to functional subpopulations of cells.

Li, Z. K.↗
NASA NTRS2021

Geospatial Cluster Analysis of the State, Duration and Severity of Drought Over Paraíba State, Northeastern Brazil

Droughts threaten water resources, agriculture, socio-economic activities and the population at the global and regional level, so identifying areas with homogeneous drought behaviors is an important consideration in improving the management of water resources. The objective of this study is to identify homogenous zones over Paraíba State in relation to the state, duration and severity of droughts that have occurred over the last 20 years (1998–2017) using hierarchical cluster analysis based on both gauge-measured and Tropical Rainfall Measuring Mission (TRMM) estimated rainfall data (TMPA 3B42). The drought series were calculated using the Standardized Precipitation Index (SPI) based on eight time scales and were grouped according to drought state, duration and severity time series. The integrated results of state, duration and severity of droughts indicate that there is a basis for dividing Paraíba State into two major regions (a) Zone I, formed by Mata Paraibana and Agreste Paraibano, and (b) Zone II, composed by Borborema and Sertão Paraibano. This division is evident when assessing short-term droughts, but in the case of long-term droughts, Paraíba State has a high similarity in terms of drought state, duration, and severity. Factors such as proximity to the ocean, active climatic systems, and the local relief configuration were identified as influencing the drought regime. Finally, it is concluded that TMPA rainfall estimates represent a valuable source of data to regionalize and identify drought patterns over this part of Brazil and that other studies of this type should be carried out to monitor these phenomena based on other satellite-based rainfall data, including the Global Precipitation Mission (GPM).

SPI↗
DOE OSTI2022

Cluster Analysis of Combined EDS and EBSD Data to Solve Ambiguous Phase Identifications

A common problem in analytical scanning electron microscopy (SEM) using electron backscatter diffraction (EBSD) is the differentiation of phases with distinct chemistry but the same or very similar crystal structure. X-ray energy dispersive spectroscopy (EDS) is useful to help differentiate these phases of similar crystal structures but different elemental makeups. However, open, automated, and unbiased methods of differentiating phases of similar EBSD responses based on their EDS response are lacking. This paper describes a simple data analytics-based method, using a combination of singular value decomposition and cluster analysis, to merge simultaneously acquired EDS + EBSD information and automatically determine phases from both their crystal and elemental data. I use hexagonal TiB 2 ceramic contaminated with multiple crystallographically ambiguous but chemically distinct cubic phases to illustrate the method. Code, in the form of a Python 3 Jupyter Notebook, and the necessary data to replicate the analysis are provided as Supplementary material.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

Statistical and clustering analysis for disturbances: A case study of voltage dips in wind farms

This study proposes and evaluates an alternative statistical methodology to analyze a large number of voltage dips. For a given voltage dip, a set of lengths is first identified to characterize the root mean square (rms) voltage evolution along the disturbance, deduced from partial linearized time intervals and trajectories. Principal component analysis and K-means clustering processes are then applied to identify rms-voltage patterns and propose a reduced number of representative rms-voltage profiles from the linearized trajectories. This reduced group of averaged rms-voltage profiles enables the representation of a large amount of disturbances, which offers a visual and graphical representation of their evolution along the events, aspects that were not previously considered in other contributions. The complete process is evaluated on real voltage dips collected in intense field-measurement campaigns carried out in a wind farm in Spain among different years. The results are included in this paper.

24 POWER TRANSMISSION AND DISTRIBUTION↗