Results for “clustering algorithm” — Page 3

NASA NTRS2006

When is Constrained Clustering Beneficial, and Why?

Several researchers have shown that constraints can improve the results of a variety of clustering algorithms. However, there can be a large variation in this improvement, even for a fixed number of constraints for a given data set. We present the first attempt to provide insight into this phenomenon by characterizing two constraint set properties: informativeness and coherence. We show that these measures can help explain why some constraint sets are more beneficial to clustering algorithms than others. Since they can be computed prior to clustering, these measures can aid in deciding which constraints to use in practice.

clustering algorithms↗

NASA NTRS2002

A Dissimilarity Measure for Clustering High- and Infinite Dimensional Data that Satisfies the Triangle Inequality

The cosine or correlation measures of similarity used to cluster high dimensional data are interpreted as projections, and the orthogonal components are used to define a complementary dissimilarity measure to form a similarity-dissimilarity measure pair. Using a geometrical approach, a number of properties of this pair is established. This approach is also extended to general inner-product spaces of any dimension. These properties include the triangle inequality for the defined dissimilarity measure, error estimates for the triangle inequality and bounds on both measures that can be obtained with a few floating-point operations from previously computed values of the measures. The bounds and error estimates for the similarity and dissimilarity measures can be used to reduce the computational complexity of clustering algorithms and enhance their scalability, and the triangle inequality allows the design of clustering algorithms for high dimensional distributed data.

Socolovsky, Eduardo A.↗

NASA NTRS2013

Reducing Earth Topography Resolution for SMAP Mission Ground Tracks Using K-Means Clustering

The K-means clustering algorithm is used to reduce Earth topography resolution for the SMAP mission ground tracks. As SMAP propagates in orbit, knowledge of the radar antenna footprints on Earth is required for the antenna misalignment calibration. Each antenna footprint contains a latitude and longitude location pair on the Earth surface. There are 400 pairs in one data set for the calibration model. It is computationally expensive to calculate corresponding Earth elevation for these data pairs. Thus, the antenna footprint resolution is reduced. Similar topographical data pairs are grouped together with the K-means clustering algorithm. The resolution is reduced to the mean of each topographical cluster called the cluster centroid. The corresponding Earth elevation for each cluster centroid is assigned to the entire group. Results show that 400 data points are reduced to 60 while still maintaining algorithm performance and computational efficiency. In this work, sensitivity analysis is also performed to show a trade-off between algorithm performance versus computational efficiency as the number of cluster centroids and algorithm iterations are increased.

ground tracks↗

NASA NTRS1974

Automated thematic mapping and change detection of ERTS-A images

The author has identified the following significant results. A diffraction pattern analysis of MSS images led to the development of spatial signatures for farm land, urban areas and mountains. Four spatial features are employed to describe the spatial characteristics of image cells in the digital data. Three spectral features are combined with the spatial features to form a seven dimensional vector describing each cell. Then, the classification of the feature vectors is accomplished by using the maximum likelihood criterion. It was determined that the recognition accuracy with the maximum likelihood criterion depends on the statistics of the feature vectors. It was also determined that for a given geographic area the statistics of the classes remain invariable for a period of a month, but vary substantially between seasons. Three ERTS-1 images from the Phoenix, Arizona area were processed, and recognition rates between 85% and 100% were obtained for the terrain classes of desert, farms, mountains, and urban areas. To eliminate the need for training data, a new clustering algorithm has been developed. Seven ERTS-1 images from four test sites have been processed through the clustering algorithm, and high recognition rates have been achieved for all terrain classes.

Gramenopoulos, N.↗

NASA NTRS2010

Operational Dynamic Configuration Analysis

Sectors may combine or split within areas of specialization in response to changing traffic patterns. This method of managing capacity and controller workload could be made more flexible by dynamically modifying sector boundaries. Much work has been done on methods for dynamically creating new sector boundaries [1-5]. Many assessments of dynamic configuration methods assume the current day baseline configuration remains fixed [6-7]. A challenging question is how to select a dynamic configuration baseline to assess potential benefits of proposed dynamic configuration concepts. Bloem used operational sector reconfigurations as a baseline [8]. The main difficulty is that operational reconfiguration data is noisy. Reconfigurations often occur frequently to accommodate staff training or breaks, or to complete a more complicated reconfiguration through a rapid sequence of simpler reconfigurations. Gupta quantified a few aspects of airspace boundary changes from this data [9]. Most of these metrics are unique to sector combining operations and not applicable to more flexible dynamic configuration concepts. To better understand what sort of reconfigurations are acceptable or beneficial, more configuration change metrics should be developed and their distribution in current practice should be computed. This paper proposes a method to select a simple sequence of configurations among operational configurations to serve as a dynamic configuration baseline for future dynamic configuration concept assessments. New configuration change metrics are applied to the operational data to establish current day thresholds for these metrics. These thresholds are then corroborated, refined, or dismissed based on airspace practitioner feedback. The dynamic configuration baseline selection method uses a k-means clustering algorithm to select the sequence of configurations and trigger times from a given day of operational sector combination data. The clustering algorithm selects a simplified schedule containing k configurations based on stability score of the sector combinations among the raw operational configurations. In addition, the number of the selected configurations is determined based on balance between accuracy and assessment complexity.

Lai, Chok Fung↗

NASA NTRS1981

Evaluation of large area crop estimation techniques

The performance of the USDAs EDITOR system is evaluated. The system processes Landsat imagery and estimates crop hectarage for large areas based on a regression estimator developed on a sample with known ground truth. It is found that use of multitemporal data over unitemporal significantly improves the hectarage estimates, and a 15% reduction in the r-squared of the regression occurs when independent and jackknifed test sets are used to evaluate the performance of the estimator. When an alternative clustering algorithm, CLASSY, is substituted for the current EDITOR method, estimator performance is improved with reduced need for analyst decisions. It is recommended that the CLASSY clustering algorithm and some form of jackknifing be implemented on EDITOR.

Amis, M. L.↗

NASA NTRS2019

Cluster Analysis of IRIS Spectroscopic Line Profiles and SDO/AIA EUV Emission in Observations and RMHD Simulations of the Solar Atmosphere

Spatially-resolved observations from the IRIS and SDO/AIA satellites, especially when coupled with realistic 3D RMHD simulations, are a powerful tool for analysis of processes in the solar chromosphere, transition region, and corona. However, the complexity of the data makes understanding the observations and modeling results difficult. In this work, we apply unsupervised clustering algorithms for analysis of observational and synthetic chromospheric Mg II h&k 2796Å&2803Å and transition region C II 1334Å&1335Å line profiles observed by IRIS, and extreme ultraviolet (EUV) emission observed by SDO/AIA, for various types of problems. The synthetic line profiles are computed for simulations of the quiescent solar atmosphere (using the StellarBox and RH1.5 codes). The K-Means clustering algorithm is applied, and the selection of an optimal number of clusters is supported by the average silhouette width technique. We discuss applications of the line profile clustering method to 1) visualization of computational and observational spectroscopic imaging data; 2) understanding of evolutionary trends and behavior patterns of quiet Sun emission and during solar flares; and 3) recognition of heating events and shock waves.

Sadykov, Viacheslav↗

NASA NTRSDate not supplied

Multipath Mitigation via Clustering for Position Estimation Refinement in Urban Environments

Position estimation using global navigation satellite systems (GNSS) suffers from poor accuracy within urban canyons due to significant signal disruption caused by tall buildings. This issue can be attributed to the GNSS signals reflecting off buildings resulting in severe multipath reflections which degrade the receiver's performance. In this paper, we introduce an innovative approach to filter GNSS satellite measurements to improve the accuracy of the estimated position by leveraging a clustering algorithm. This approach utilizes a predictive GNSS availability service to filter out non-line-of-sight measurements. Then, a subset of line-of-sight satellite measurement combinations are evaluated using a clustering algorithm. When combined, results show these techniques can reduce the mean horizontal error measured in an urban canyon by nearly an order of magnitude, from ~ 18 meters to ~ 2 meters when using a single point positioning solver.

GPS↗

NASA NTRSDate not supplied

Multipath Mitigation via Clustering for Position Estimation Refinement in Urban Environments

Position estimation using global navigation satellite systems (GNSS) suffers from poor accuracy within urban canyons due to significant signal disruption caused by tall buildings. This issue can be attributed to the GNSS signals reflecting off buildings resulting in severe multipath reflections which degrade the receiver's performance. In this paper, we introduce an innovative approach to filter GNSS satellite measurements to improve the accuracy of the estimated position by leveraging a clustering algorithm. This approach utilizes a predictive GNSS availability service to filter out non-line-of-sight measurements. Then, a subset of line-of-sight satellite measurement combinations are evaluated using a clustering algorithm. When combined, results show these techniques can reduce the mean horizontal error measured in an urban canyon by nearly an order of magnitude, from ~ 18 meters to ~ 2 meters when using a single point positioning solver.

GPS↗

NASA NTRS1975

Computer-aided analysis of Landsat-1 MSS data - A comparison of three approaches, including a 'modified clustering' approach

Three approaches for analyzing Landsat-1 data from Ludwig Mountain in the San Juan Mountain range in Colorado are considered. In the 'supervised' approach the analyst selects areas of known spectral cover types and specifies these to the computer as training fields. Statistics are obtained for each cover type category and the data are classified. Such classifications are called 'supervised' because the analyst has defined specific areas of known cover types. The second approach uses a clustering algorithm which divides the entire training area into a number of spectrally distinct classes. Because the analyst need not define particular portions of the data for use but has only to specify the number of spectral classes into which the data is to be divided, this classification is called 'nonsupervised'. A hybrid method which selects training areas of known cover type but then uses the clustering algorithm to refine the data into a number of unimodal spectral classes is called the 'modified-supervised' approach.

Fleming, M. D.↗

NASA NTRS1993

Two generalizations of Kohonen clustering

The relationship between the sequential hard c-means (SHCM), learning vector quantization (LVQ), and fuzzy c-means (FCM) clustering algorithms is discussed. LVQ and SHCM suffer from several major problems. For example, they depend heavily on initialization. If the initial values of the cluster centers are outside the convex hull of the input data, such algorithms, even if they terminate, may not produce meaningful results in terms of prototypes for cluster representation. This is due in part to the fact that they update only the winning prototype for every input vector. The impact and interaction of these two families with Kohonen's self-organizing feature mapping (SOFM), which is not a clustering method, but which often leads ideas to clustering algorithms is discussed. Then two generalizations of LVQ that are explicitly designed as clustering algorithms are presented; these algorithms are referred to as generalized LVQ = GLVQ; and fuzzy LVQ = FLVQ. Learning rules are derived to optimize an objective function whose goal is to produce 'good clusters'. GLVQ/FLVQ (may) update every node in the clustering net for each input vector. Neither GLVQ nor FLVQ depends upon a choice for the update neighborhood or learning rate distribution - these are taken care of automatically. Segmentation of a gray tone image is used as a typical application of these algorithms to illustrate the performance of GLVQ/FLVQ.

Bezdek, James C.↗

NASA NTRS2014

Clustering Days with Similar Airport Weather Conditions

On any given day, traffic flow managers must often rely on past experience and intuition when developing traffic flow management initiatives that mitigate imbalances between the aircraft demand and the weather impacted airport capacity. The goal of this study was to build on recent efforts to apply data mining classification and clustering algorithms to vast archives of historical weather and air traffic data to identify patterns and past decisions that can ultimately inform day-of-operations decision-making. More specifically, this study identified similar weather impacted days at select U.S. airports, and analyzed the traffic management initiatives implemented on these representative days. The identification of the similar days was accomplished by applying a decision tree algorithm to the hourly Localized Aviation Model Output Statistics Program observations and the arrival delays for Newark Liberty International Airport. The branches from the trained decision tree were subsequently pruned to identify four weather conditions that resulted in medium to high delays for the arrivals scheduled to Newark in 2012. Using these weather conditions, four, daily airport-level Weather Impacted Traffic Index values were calculated using the Localized Aviation Model Output Statistics Program observations and the 2012 scheduled arrival counts from the FAAs Aviation System Performance Metric system. The four, daily Weather Impacted Traffic Index values for 2012 were subsequently clustered using an Expectation Maximization clustering algorithm, and nine unique types of weather days at Newark were identified. By far the most prominent type of day at Newark was a day associated with relatively good weather conditions, where there was little convective activity, winds were low, ceilings and visibility were high and there was little precipitation. Moderate levels of convective activity characterized the next most prominent type of day. Days with persistently high winds or low ceiling and visibility levels were relatively rare in 2012. Lastly, the frequency at which Ground Delay Programs, Ground Stops and Miles-in-Trail restrictions were implemented on each of the typical types of days at Newark were analyzed. Based on the results, it does appear as if the usage of Miles-in-Trail, Ground Delay Program and Ground Stop restrictions correlates well with the severity of the weather associated with each unique type of weather impacted day at Newark. Furthermore, the results demonstrate that it is feasible to use historical weather and air traffic archives to provide guidance on the types of traffic management restrictions to implement in response to the weather conditions impacting an airport.

traffic flow management↗

NASA NTRS2014

Clustering Days with Similar Airport Weather Conditions

On any given day, traffic flow managers must often rely on past experience and intuition when developing traffic flow management initiatives that mitigate imbalances between the aircraft demand and the weather impacted airport capacity. The goal of this study was to build on recent efforts to apply data mining classification and clustering algorithms to vast archives of historical weather and air traffic data to identify patterns and past decisions that can ultimately inform day-of-operations decision-making. More specifically, this study identified similar weather impacted days at select U.S. airports, and analyzed the traffic management initiatives implemented on these representative days. The identification of the similar days was accomplished by applying a decision tree algorithm to the hourly Localized Aviation Model Output Statistics Program observations and the arrival delays for Newark Liberty International Airport. The branches from the trained decision tree were subsequently pruned to identify four weather conditions that resulted in medium to high delays for the arrivals scheduled to Newark in 2012. Using these weather conditions, four, daily airport-level Weather Impacted Traffic Index values were calculated using the Localized Aviation Model Output Statistics Program observations and the 2012 scheduled arrival counts from the FAAs Aviation System Performance Metric system. The four, daily Weather Impacted Traffic Index values for 2012 were subsequently clustered using an Expectation Maximization clustering algorithm, and nine unique types of weather days at Newark were identified. By far the most prominent type of day at Newark was a day associated with relatively good weather conditions, where there was little convective activity, winds were low, ceilings and visibility were high and there was little precipitation. Moderate levels of convective activity characterized the next most prominent type of day. Days with persistently high winds or low ceiling and visibility levels were relatively rare in 2012. Lastly, the frequency at which Ground Delay Programs, Ground Stops and Miles-in-Trail restrictions were implemented on each of the typical types of days at Newark were analyzed. Based on the results, it does appear as if the usage of Miles-in-Trail, Ground Delay Program and Ground Stop restrictions correlates well with the severity of the weather associated with each unique type of weather impacted day at Newark. Furthermore, the results demonstrate that it is feasible to use historical weather and air traffic archives to provide guidance on the types of traffic management restrictions to implement in response to the weather conditions impacting an airport.

weather↗

NASA NTRS2016

An Efficient, FPGA-Based, Cluster Detection Algorithm Implementation for a Strip Detector Readout System in a Time Projection Chamber Polarimeter

A fundamental challenge in a spaceborne application of a gas-based Time Projection Chamber (TPC) for observation of X-ray polarization is handling the large amount of data collected. The TPC polarimeter described uses the APV-25 Application Specific Integrated Circuit (ASIC) to readout a strip detector. Two dimensional photoelectron track images are created with a time projection technique and used to determine the polarization of the incident X-rays. The detector produces a 128x30 pixel image per photon interaction with each pixel registering 12 bits of collected charge. This creates challenging requirements for data storage and downlink bandwidth with only a modest incidence of photons and can have a significant impact on the overall mission cost. An approach is described for locating and isolating the photoelectron track within the detector image, yielding a much smaller data product, typically between 8x8 pixels and 20x20 pixels. This approach is implemented using a Microsemi RT-ProASIC3-3000 Field-Programmable Gate Array (FPGA), clocked at 20 MHz and utilizing 10.7k logic gates (14% of FPGA), 20 Block RAMs (17% of FPGA), and no external RAM. Results will be presented, demonstrating successful photoelectron track cluster detection with minimal impact to detector dead-time.

Polarimeter↗

NASA NTRS1981

ICAP - An Interactive Cluster Analysis Procedure for analyzing remotely sensed data

An Interactive Cluster Analysis Procedure (ICAP) was developed to derive classifier training statistics from remotely sensed data. ICAP differs from conventional clustering algorithms by allowing the analyst to optimize the cluster configuration by inspection, rather than by manipulating process parameters. Control of the clustering process alternates between the algorithm, which creates new centroids and forms clusters, and the analyst, who can evaluate and elect to modify the cluster structure. Clusters can be deleted, or lumped together pairwise, or new centroids can be added. A summary of the cluster statistics can be requested to facilitate cluster manipulation. The principal advantage of this approach is that it allows prior information (when available) to be used directly in the analysis, since the analyst interacts with ICAP in a straightforward manner, using basic terms with which he is more likely to be familiar. Results from testing ICAP showed that an informed use of ICAP can improve classification, as compared to an existing cluster analysis procedure.

Wharton, S. W.↗

NASA NTRS2005

Constrained spectral clustering under a local proximity structure assumption

This work focuses on incorporating pairwise constraints into a spectral clustering algorithm. A new constrained spectral clustering method is proposed, as well as an active constraint acquisition technique and a heuristic for parameter selection. We demonstrate that our constrained spectral clustering method, CSC, works well when the data exhibits what we term local proximity structure.

domain knowledge↗

NASA NTRS2021

PIXLISE-C: Exploring The Data Analysis Needs of NASA Scientists for Mineral Identification

NASA JPL scientists working on the micro x-ray fluorescence (microXRF) spectroscopy data collected from Mars surface perform data analysis to look for signs of past microbial life on Mars. Their data analysis workflow mainly involves identifying mineral com- pounds through the element abundance in spatially distributed data points. Working with the NASA JPL team, we identified pain points and needs to further develop their existing data visualization and analysis tool. Specifically, the team desired improvements for the process of creating and interpreting mineral composition groups. To address this problem, we developed an interactive tool that enables scientists to (1) cluster the data using either manual lasso-tool selection or through various machine learning clustering algorithms, and (2) compare the clusters and individual data points to make informed decisions about mineral compositions. Our preliminary tool supports a hybrid data analysis workflow where the user can manually refine the machine-generated clusters.

Davidoff, Scott↗

NASA NTRS1983

LANDSAT-4 image data quality analysis

Seven heterogeneous areas within the entire Des Moines, Iowa test site were selected to define candidate spectral training classes using a clustering algorithm. In addition to the 91 cluster nonsupervised classes, three supervised training classes were defined. The original candidate training classes were reduced to 42 spectrally separable training classes. The minimum and average transformed divergence values for the 42 spectral classes and for the best subsets of Y TM spectral bands are shown in a table. The best spectral band for any combination of 1 through 7 bands is the first middle IR band. The next best band is the near IR, followed by the red band and than the thermal IR. The best combination of four bands includes one from each of the four regions of the spectrum (visible, near IR, middle IR, and thermal IR).

Anuta, P. E.↗