Multivariate Clustering Technique Research Articles

Multivariate Biplot Analysis for the Diversity in Bread Wheat Genotypes (Triticum aestivum L.)

A field study was conducted during (November–April, 2017–2018) at CCSHAU, Hisar, Haryana, Indiaxxx to evaluate wheat genotypes for diversity analysis by multivariate and biplot analysis.xxx The morphological and agronomic attributes of wheat have been evaluated to measure genetic variation and their close relatives. Diversity analysis of bread wheat genotypes was deciphered by fifteen morphological traits utilizing multivariate hierarchical clustering technique and biplot analysis. Multivariate analyses have been applied to measure the diversity in wheat accessions and to evaluate the relative contributions of morphological traits to the total variability in a collection. Recent analyses enable accessions to be classified into clusters as per their similarity indexes. Good amount of variability had been observed for most of the traits. Maximum range was observed for flag leaf area and plant height. The positive correlation of the grain yield with various traits ensured indirect selection for yield can be done by selecting those characters. Significant positive correlation of grain yield exhibited with number of grains spike-1, thousands grain weight and plant height. Multivariate Hierarchical clustering technique by Ward’s method expressed three clusters with 20.4, 20.5 and 59.1% of total genotypes. The first principal component accounted for 29.4% with major traits number of tillers plant-1, flag leaf area, biological yield plant-1, grain yield plant-1, weight of grain ear-1, flag leaf length. The contribution of second was 15.7% with major six traits number of grains ear-1, ear weight, weight of grain ear-1, number of spikelet ear-1, grain yield plant-1 and number of tillers plant-1.

Demand charges and user flexibility – Exploring differences in electricity consumer types and load patterns within the Swedish commercial sector

Demand-based charges have been employed as a tool intended to reduce electricity users’ maximum demand but there is a lack of consensus regarding their efficacy. One reason for this may be the diversity in the flexibility potential of different types of users. This study explores the flexibility potential of different types of electricity consumers in the small to medium-sized commercial sector (35-63A) in response to a compulsory demand charge. The objective is to characterize varying levels of flexibility with respect to different types of commercial users with different load patterns. A multivariate clustering technique was used to group commercial users with comparable load patterns based on a year of hourly data before the tariff change was introduced. This method was used to: (1) match users from the intervention area and reference area with similar load patterns, without losing any user data, and (2) compare how users with different load patterns react differently to the tariff change. We found clear distinctions in the types of commercial users in each cluster and their response to the tariff, demonstrating the extent to which demand flexibility may be dependent on the nature of an organization’s activities and its respective load patterns. The highest demand flexibility was found in clusters which had a large share of users in the IT sector, commerce and public administration. The lowest demand flexibility was found in the real estate and education sectors. Future research should further investigate these variations and explore the possibilities of tailoring interventions to the specific types of users.

Exploring Grower-sourced Data to Understand Spatiotemporal Trends in the Occurrence of a Vector, Pseudococcus maritimus (Hemiptera: Pseudococcidae) and Improve Grapevine Leafroll Disease Management.

Grapevine leafroll disease is a significant concern in the wine grape industry, as it spreads rapidly and contributes to economically significant reductions in yield and grape quality. Our objective was to utilize 5 yr of grower-sourced data from Napa (California, USA) to improve local and regional disease management efforts. Specifically, we applied a spatially integrated multivariate clustering technique to improve understanding of spatiotemporal trends in Pseudococcus maritimus (Ehrhorn) male populations-the primary vector in the region. We also implemented generalized linear mixed models to evaluate the effects of two key practices, insecticide sprays and roguing, on disease incidence. Results show P. maritimus has a biannual flight pattern in the study area, with the first flight peaking in early May and the second between early August and early September. Clusters of P. maritimus flight data fall largely within the vineyard footprints of individual growers, but also showed clear neighborhood effects. We found that when disease incidence within a block is <1%, consistent monitoring and removal of diseased vines is required to contain within-block spread. As within-block disease incidence grows to 1-20%, both insecticide applications and roguing are effective practices to reduce spread. At incidence levels >20%, roguing is a critical practice. Our results emphasize the importance of individual management efforts, but also the value of programs that engage the wider neighboring community and highlight the power of community data collection to guide decision-making.

Truck platooning in the U.S. national road network: A system-level modeling approach

Truck platooning enables a group of trucks to move close together, which helps reduce truck fuel use and increase effective road capacity. In this paper, a system-level equilibrium model is developed to characterize spontaneous truck platooning with coexistence of non-platooning vehicles in a network, by explicitly accounting for the interlocking relationship among platoon formation time, truck fuel saving, and increase in effective road capacity. To equilibrate the relationships, an algorithm is proposed which involves a diagonalization approach and a bush-based algorithm to solve decomposed subproblems. The condition of proportionality is imposed to obtain unique traffic flows for each class of vehicles on road links. In addition, a spatially constrained multivariate clustering technique is employed to construct origin/destination zones that are smaller than the coarse Freight Analysis Framework (FAF) zones, while maintaining reasonable computational burden for network traffic assignment. Model implementation in the U.S. shows that platooning could lead to 7.9% fuel saving among platoonable trucks in 2025 and a comparable increase in effective capacity of platoonable road links, which would account for 60% of rural interstate roads. The fuel saving and road capacity improvement translate into an annual cost reduction of $868 million for the U.S. intercity trucking sector and reduced road infrastructure investment needs worth $4.8 billion. Extensive sensitivity analysis further reveals that fuel saving of platoonable trucks increases with platoon size but decreases with inter-truck distance in a platoon. Fuel saving potential suggests that priority should be given to rural rather than urban roads in deploying platooning technologies. As expected, greater market penetration of platooning technologies means higher fuel saving and greater increase in effective road capacity.

Objectively Determining States of the Solar Wind Using Machine Learning

Abstract Conclusively determining the states of the solar wind will aid in tracing the origins of those states to the Sun, and in the process help to find the wind’s origin and acceleration mechanism(s). Prior studies have characterized the various states of the wind, making lists that are only partially based on objective criteria; different approaches obtain substantially different results. To uncover the unbiased states of the solar wind, we use “k-means clustering”—an unsupervised machine learning method—including constructed multipoint variables. The method allows exploration of different descriptive state variables and numbers of fundamental states (clusters). We show that the clusters reveal structures similar to those found by more ad hoc means, including coronal hole wind, interplanetary coronal mass ejections, “slow wind” (better: noncoronal hole flow), “pseudostreamers,” and stream interaction regions, but with differences that should be useful in refining our previous ideas. These results demonstrate the viability of the approach and warrant further study to understand the origin of remaining discrepancies. Complexity in k-means characterization of the wind may ultimately point to complexity at the source; studies closer to the Sun with Parker Solar Probe will help. We confirm the utility of a set of variables that can serve as a proxy for composition measurements. This proxy permits studies at high time resolution and where composition is not available. This and our recently developed unsupervised multivariate clustering technique are expected to be beneficial in the automated identification of structures and events in a variety of studies.

Chemical change during storage of crambe cultivated with application of plant growth regulators

This study aimed to characterize the chemical composition of crambe grains produced with plant growth regulators in the 2014 and 2015 harvests and stored for 180 days. During crambe cultivation, two foliar sprayings were carried out with the following treatments: 1) control treatment with distilled water; 2) indole-3-acetic acid 100 mg L-1 (IAA); 3) 3-gibberellic acid P.A. 100 mg L-1 (GA3); 4) Stimulate®. After harvesting, the grains were analyzed initially and after 180 days of uncontrolled storage. We checked water content, lipids in hot extraction (HE), lipids in cold extraction (CE), proteins, acidity and antioxidant activity. The Tukey test was used to compare the averages (p&lt;0.05) and the parameters were grouped using a multivariate clustering technique presented in dendogram. The CE and HE lipid contents were reduced after storage, whereas the protein content and antioxidant activity of the grains were not altered by storage. However, the grains that received IAA application showed an increase in the CE lipid content and their preservation after storage. GA3 application, on the other hand, reduced the protein content. Lipid contents remained constant at the end of storage, and the acidity was reduced due to the application of GA3 and Stimulate® plant growth regulators. The 180-day storage changed crambe quality and regulators can assist in preserving grain quality during storage. The CE and HE lipid content and the grain acidity were good indicators for evaluating effects.

EcoAnthromes of Alberta: An example of disturbance-informed ecological regionalization using remote sensing

Humans influence ecosystems on magnitudes that often exceed that of natural forces such as climate and geology; however, frameworks rarely include anthropogenic disturbance when delineating unique ecological regions. A critical step toward understanding, managing and monitoring human-altered ecosystems is to incorporate disturbance into ecological regionalizations. Furthermore, quantitative regionalization approaches are desirable to provide cost-effective, repeatable and statistically sound stratification for environmental monitoring. We applied a two-stage multivariate clustering technique to identify ‘EcoAnthromes’ across a large area – the province of Alberta, Canada – at 30 m spatial resolution, and using primarily remotely sensed inputs. The EcoAnthrome clusters represent regions with unique ecological characteristics based on a combination of natural ecological potential (e.g., climatic and edaphic factors) and disturbance, both natural and anthropogenic. Compared to existing expert-derived Natural Subregions in Alberta, the model-based EcoAnthromes showed greater class separation and explained more variance for an assortment of variables related to land cover, disturbance and species intactness. The EcoAnthromes successfully separated important ecological regions that are defined by complex assemblages of topography, climate and disturbance, such as gravel-bed river valleys, boreal forests, grasslands, post-fire recovery areas and highly disturbed agricultural, industrial and urban landscapes. In addition to presenting a flexible method for EcoAnthrome regionalization, we group and describe the EcoAnthromes created for Alberta and discuss how they can complement expert-derived regionalizations to aid in environmental management efforts, such as species recovery planning and monitoring for threatened species.

A machine learning based stochastic optimization framework for a wind and storage power plant participating in energy pool market

Renewable energy plants can participate in the energy pool market including day-ahead, intraday and balancing markets. The aim of this work is to develop a decision-making framework for a Wind and Storage Power Plant participating in the pool market to handle the uncertainty associated with the parameters of energy price and available wind energy, which are not known when decisions are to be made. Thus, the problem of maximizing the net income of such a plant participating in the pool market is formulated as a two-stage convex stochastic program. A novel hybrid approach using multivariate clustering technique and recurrent neural network is used to derive scenarios to handle the uncertainty associated with the energy price. Lastly, simulation experiments are carried out to show the effectiveness of the proposed methods using a real-world case study. Operators of variable renewable resource generators could use the proposed framework to make robust decisions and better manage their operations to gain competitive advantage.

Agrupamento multivariado de curvas na desidratação em raízes de batata-doce

Minimizing post-harvest losses during sweet potato storage is imperative for the producer and consumer. Therefore, the use of statistical tools to select the superior genotypes that have good post-harvest characteristics will contribute to breeding programs. The aim of this study was to demonstrate the applicability of the multivariate clustering technique as an alternative in the post-harvest study on root dehydration among sweet potato accessions. A total of 74 accessions of sweet potatoes were evaluated in a randomized complete block design in four replicates. The roots were stored in plastic boxes at room temperature and the loss of fresh matter was measured on times 0, 4, 8, 12 and 16 days after harvest. The multivariate clustering was based on the Euclidean distance and the Tocher optimization method on adjusted curves using linear and nonlinear models. The non-linear best fit model was Brody, which allowed the discrimination of accesses with higher and lower dehydration. The multivariate grouping of curves was efficient in the post-harvest study on sweet potato access roots.

Sleep spindle detection using multivariate Gaussian mixture models.

In this research study we have developed a clustering-based automatic sleep spindle detection method that was evaluated on two different databases. The databases consisted of 20 all-night polysomnograph recordings. Past detection methods have been based on subject-independent and some subject-dependent parameters, such as fixed or variable thresholds to identify spindles. Using a multivariate Gaussian mixture model clustering technique, our algorithm was developed to use only subject-specific parameters to detect spindles. We have obtained an overall sensitivity range (65.1-74.1%) at a (59.55-119.7%) false positive proportion.

A Two Stage Heuristic Solution Approach for Resource Assignment during a Cell Formation Problem

Design of Cellular Manufacturing System involves four major decisions: Cell formation (CF), Group layout (GL), Group scheduling (Gs) and Resource assignment (RA). These problems should be regarded, concurrently, in order to obtain an optimal solution in a CM environment. In this paper a two stage heuristic procedure is proposed for CF and RA decisions problem. The solution approach contains a heuristic multivariate clustering technique as the first stage to find the best machine-cluster center distances. Next in the second stage a new mathematical model based on extracted distances and also worker related issues, including salary, hiring, firing and cross-training is proposed. In order to verify and validate the performance of proposed approach a mathematical model considering the inter-intra cell part trips and also operator related issues are developed and some numerical examples are solved using Lingo Software. The analysis of results verifies the solution approach in both optimality and computational time aspects.

Hybrid principal component analysis technique to machine-part grouping problem in cellular manufacturing system

This article portrays a hybrid principal component analysis (PCA)-based technique to construct production cells in cellular manufacturing system (CMS). The key problem in CMS is to recognise the machine cells and corresponding part families and subsequently the formation of production cells. A novel approach is considered in this study to systematise a hybrid multivariate clustering technique based on covariance analysis to form the machine cells in CMS. The intended technique is demonstrated in three segments. Firstly, a similarity matrix is developed by exploiting the covariance analysis procedure. In the second stage, the PCA is utilised to identify the potential clusters in CMS with the assistance of eigenvalue and eigenvector computation. In the last stage, an adjustment heuristic is adopted to improve the solution quality and consequently the clustering efficiency. This article states that, the addition of the adjustment heuristic approach into a traditional multivariate PCA-based clustering technique not only enhances the solution quality significantly, but also downgrades the inconsistency of the solutions achieved. The hybrid technique is tested on 24 test datasets available in published articles and it is shown to outperform other published methodologies by enhancing the solution quality on the test problems.

An introduction to wavelets with applications to Andrews; plots

A brief introduction to wavelets targeted to the statistician is given. Several types of wavelets are described and compared with the Fourier functions. It is shown how wavelets might extend the multivariate clustering technique of Andrews' plots.

Rapid and quantitative analysis of metabolites in fermentor broths using pyrolysis mass spectrometry with supervised learning: application to the screening of Penicillium chrysogenum fermentations for the overproduction of penicillins

The combination of pyrolysis mass spectrometry (PyMS) and artificial neural networks (ANNs) can be used to quantify levels of penicillins in strains of Penicillium chrysogenum and ampicillin in spiked samples of Escherichia coli. Four P. chrysogenum strains (NRRL 1951, Wis Q176, P1, and P2) were grown in submerged culture to produce penicillins, and fermentation samples were taken aseptically and subjected to PyMS. To deconvolute the pyrolysis mass spectra so as to obtain quantitative information on the titre of penicillins, fully-interconnected feedforward artificial neural networks (ANNs) were studied; the weights were modified using the standard back-propagation algorithm, and the nodes used a sigmoidal squashing function. In addition the multivariate linear regression techniques of partial least squares regression (PLS), principal components regression (PCR) and multiple linear regression (MLR) were applied. The ANNs could be trained to give excellent estimates for the penicillin titre, not only from the spectra that had been used to train the ANN but more importantly from previously unseen pyrolysis mass spectra. All the linear regression methods failed to give accurate predictions, because of the very variable biological backgrounds (the four different strains) in which penicillin was produced and also of the inability of models using linear regression accurately to map non-linearities. Comparisons of squashing functions on the output nodes of identical 150-8-1 neural networks revealed that networks employing linear functions gave more accurate estimates of ampicillin in E. coli near the edges of the concentration range than did those using sigmoidal functions. It was also shown that these neural networks could be successfully used to extrapolate beyond the concentration range on which they had been trained. PyMS with the multivariate clustering technique of principal components analysis was able to differentiate between four strains of P. chrysogenum studied, and was also able to detect phenotypic differences at five, seven, nine or 11 days growth. A crude smapling procedure consisting of homogenised agar plugs proved applicable for rapid analysis of a large number of samples.

Multivariate Cluster Analysis of Pharmaceutical Formulation Data Using Andrews Plots

A multivariate clustering technique known as the Andrews Plot was applied to a set of previously published data describing a series of pharmaceutical tablet formulations. Dependent variable data were subjected to a trigonometric transform algorithm and unique sinusoidal patterns were obtained for each of 18 formulations characterized by a total of 12 granulation and tablet parameters. Three main formulation clusters were observed in close agreement with a previously published analysis of the same data set using principal components methodology. Interpretation of cluster behavior lead to conclusions closely aligned to those of the original investigators, although some alternate hypotheses also arise. Limitations of the Andrews plotting technique were explored. The method appears to have value in the analysis of complex data sets derived from pharmaceutical formulation characterizations.

Multivariate analysis of the MMPI profiles of patients with multiple pain complaints.

The MMPI profiles of 123 male and 221 female patients with multiple pain complaints referred for psychological evaluation over a three-year period were examined using a multivariate clustering technique. Replicable subgroups were found in the patient cohorts in both the male and female samples. Multiple discriminant analysis and analysis of variance showed that the subgroups differed significantly from one another and within subgroup variation was small relative to between subgroup variation. It is suggested that pain patients are characterized by a variety of personality styles; subgroup membership may be an indication of behavior and personality dynamics which may have implications for treatment selection. Inferences as to possible treatment responses based on subgroup membership are made.

Relative Growth in Two Sympatric Species of Sceloporus

Osteological measurements on a growth series of two sympatric species of lizards, Sceloporus undulatus (maximum snout to sacrum length 59.1 mm) and S. olivaceous (maximum length 98.5 mm) were subjected to bivariate and multivariate morphometric analysis. Major identified sources of variability in the -sample are size, sex and taxonomy, the same that a paleontologist has to deal with. The two species are very similar osteologically but differ in size. Mixed bivariate plots show a separation between the two species for only one of 26 characters, but that character, as predicted, was strongly allometric. The single bivariate plot provided a more sensitive taxonomic separation than did a multivariate clustering technique, principal coordinates analysis. The latter did suggest, however, the existence of sexual dimorphism, previously unreported, which was confirmed by canonical analysis. Though the two species differ in maximum size by a factor of 1.5 to 1.7, the larger species is a geometrically scaled-up replica of the smaller one for only three of 28 relationships measured; many of the previously reported structural differences between species relate to the comparison between adults of the smaller species and juveniles of the larger. The effectiveness of jaw adduction of both species increases significantly through life, and positive allometry of limb length correlates with increased size of home range during ontogeny. INTRODUCTION This report is the second in a series of studiesi (Dodson, 1975a, b and c) designed to provide the paleontologist with a basis for understanding the kinds of variability potentially represented in a sample of fossils that spans a significant size range. The ultimate goal of the larger project is to test whether application of principles of ontogenetic allometry can demonstrate that 12 species of lambeosaurine hadrosaurs (duck-billed dinosaurs) of variable size and shape from a single formation may actually constitute a single ontogenetic series. The approach that is followed in all of the studies is explicitly morphometric, with a large number of cranial and po-stcranial variables being measured. In a study of a growth series of Alligator (Dodson, 1975a), ontogenetic changes of shape were described by means of allometric coefficients, and many of the changes were found, by bivariate and multivariate methods, to have expression in the function and ecology of the animal as it grows from hatchling to large size. In the present study data on two sympatric species of lizard, Sceloporus olivaceous and S. undulatus, are examined. The data are of considerable interest, for the two species are morphologically very similar, a growth series of each is included, and each specimen is 1 Present address: Laboratories of Anatomy, School of Veterinary Medicine, University of Pennsylvania, Philadelphia, Pa. 19174.