Covers everything readers need to know about clustering methodology for symbolic data—including new methods and headings—while providing a focus on multi-valued list data, interval data and histogram ...dataThis book presents all of the latest developments in the field of clustering methodology for symbolic data—paying special attention to the classification methodology for multi-valued list, interval-valued and histogram-valued data methodology, along with numerous worked examples. The book also offers an expansive discussion of data management techniques showing how to manage the large complex dataset into more manageable datasets ready for analyses.Filled with examples, tables, figures, and case studies, Clustering Methodology for Symbolic Data begins by offering chapters on data management, distance measures, general clustering techniques, partitioning, divisive clustering, and agglomerative and pyramid clustering. Provides new classification methodologies for histogram valued data reaching across many fields in data scienceDemonstrates how to manage a large complex dataset into manageable datasets ready for analysisFeatures very large contemporary datasets such as multi-valued list data, interval-valued data, and histogram-valued dataConsiders classification models by dynamical clusteringFeatures a supporting website hosting relevant data sets Clustering Methodology for Symbolic Data will appeal to practitioners of symbolic data analysis, such as statisticians and economists within the public sectors. It will also be of interest to postgraduate students of, and researchers within, web mining, text mining and bioengineering.
A 1932 editorial in Poultry Science stated that sampling theory, or experimental power, could be useful for “the investigator to know how many … birds to put into each experimental pen.” ...Nevertheless, in the past 90 yr, appropriate experimental power estimates have rarely been applied to research with poultry. To estimate the overall variation and appropriate use of resources with animals in pens, a nested analysis should be conducted. Bird-to-bird and separate pen-to-pen variances were separated for 2 datasets, one from Australia and one from North America. The implications of using variances for birds per pen and pens per treatments are detailed. With 5 pens per treatment, increasing birds per pen from 2 to 4 decreased the SD from 183 to 154, but increasing birds/pen from 100 to 200 only decreased the SD from 70 to 60. With 15 birds per treatment, increasing pens/treatment from 2 to 3 decreased SD from 140 to 126, but increasing pens/treatment from 11 to 12 only decreased the SD from 91 to 89. Choosing the number of birds to include in any study should be based on expectations from historical data and the amount of risk investigators are prepared to accept. Too little replication will not allow relatively small differences to be detected. On the other hand, too much replication is wasteful in terms of birds and resources, and violates the fundamental principles of the ethical use of animals in research. Two general conclusions can be made from this analysis. First, it is very difficult to detect 1% to 3% differences in broiler chicken body weight with only one experiment consistently because of inherent genetic variability. Second, increasing either birds per pen or pens per treatment decreased the SD in a diminishing returns fashion. The example presented here is body weight, of primary importance to production agriculture, but it is applicable whenever a nested design is used (multiple samples from the same bird or tissue, etc.).
In recent years, metagenomic Next-Generation Sequencing (mNGS) has increasingly been used for an accurate assumption-free virological diagnosis. However, the systematic workflow evaluation on ...clinical respiratory samples and implementation of quality controls (QCs) is still lacking.
A total of 3 QCs were implemented and processed through the whole mNGS workflow: a no-template-control to evaluate contamination issues during the process; an internal and an external QC to check the integrity of the reagents, equipment, the presence of inhibitors, and to allow the validation of results for each sample. The workflow was then evaluated on 37 clinical respiratory samples from patients with acute respiratory infections previously tested for a broad panel of viruses using semi-quantitative real-time PCR assays (28 positive samples including 6 multiple viral infections; 9 negative samples). Selected specimens included nasopharyngeal swabs (n = 20), aspirates (n = 10), or sputums (n = 7).
The optimal spiking level of the internal QC was first determined in order to be sufficiently detected without overconsumption of sequencing reads. According to QC validation criteria, mNGS results were validated for 34/37 selected samples. For valid samples, viral genotypes were accurately determined for 36/36 viruses detected with PCR (viral genome coverage ranged from 0.6 to 100%, median = 67.7%). This mNGS workflow allowed the detection of DNA and RNA viruses up to a semi-quantitative PCR Ct value of 36. The six multiple viral infections involving 2 to 4 viruses were also fully characterized. A strong correlation between results of mNGS and real-time PCR was obtained for each type of viral genome (R
ranged from 0.72 for linear single-stranded (ss) RNA viruses to 0.98 for linear ssDNA viruses).
Although the potential of mNGS technology is very promising, further evaluation studies are urgently needed for its routine clinical use within a reasonable timeframe. The approach described herein is crucial to bring standardization and to ensure the quality of the generated sequences in clinical setting. We provide an easy-to-use single protocol successfully evaluated for the characterization of a broad and representative panel of DNA and RNA respiratory viruses in various types of clinical samples.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Increasingly, datasets are so large they must be summarized in some fashion so that the resulting summary dataset is of a more manageable size, while still retaining as much knowledge inherent to the ...entire dataset as possible. One consequence of this situation is that the data may no longer be formatted as single values such as is the case for classical data, but rather may be represented by lists, intervals, distributions, and the like. These summarized data are examples of symbolic data. This article looks at the concept of symbolic data in general, and then attempts to review the methods currently available to analyze such data. It quickly becomes clear that the range of methodologies available draws analogies with developments before 1900 that formed a foundation for the inferential statistics of the 1900s, methods largely limited to small (by comparison) datasets and classical data formats. The scarcity of available methodologies for symbolic data also becomes clear and so draws attention to an enormous need for the development of a vast catalog (so to speak) of new symbolic methodologies along with rigorous mathematical and statistical foundational work for these methods.
1. Research papers use a variety of methods for evaluating experiments designed to determine nutritional requirements of poultry. Growth trials result in a set of ordered pairs of data. Often, ...point-by-point comparisons are made between treatments using analysis of variance. This approach ignores that response variables (body weight, feed efficiency, bone ash, etc.) are continuous rather than discrete. Point-by-point analyses harvest much less than the total amount of information from the data. Regression models are more effective at gleaning information from data, but the concept of “requirements” is poorly defined by many regression models. 2. Response data from a study of the lysine requirements of young broilers was used to compare methods of determining requirements. In this study, multiple range tests were compared with quadratic polynomials (QP), broken line models with linear (BLL) or quadratic (BLQ) ascending portions, the saturation kinetics model (SK) a logistic model (LM) and a compartmental (CM) model. 3. The sum of total residuals squared was used to compare the models. The SK and LM were the best fit models, followed by the CM, BLL, BLQ, and QP models. A plot of the residuals versus nutrient intake showed clearly that the BLQ and SK models fitted the data best in the important region where the ascending portion meets the plateau. 4. The BLQ model clearly defines the technical concept of nutritional requirements as typically defined by nutritionists. However, the SK, LM and CM models better depict the relationship typically defined by economists as the “law of diminishing marginal productivity”. The SK model was used to demonstrate how the law of diminishing marginal productivity can be applied to poultry nutrition, and how the “most economical feeding level” may replace the concept of “requirements”.
Although it is 45 years since legislation made gender discrimination on university campuses illegal, salary inequities continue to exist today. The seminal work in studying the existence of salary ...inequities is that of the American Association of University Professors (AAUP), by Scott (
1977
) and Gray (
1980
). Subsequently, innumerable analyses based on versions of their multiple regression model have been published. Salary is the dependent variable and is modeled to depend on various independent predictor variables such as years employed. Often, indicator terms, for gender and/or discipline are included in the model as independent predicator variables. Unfortunately, many of these studies are not well grounded in basic statistical science. The most glaring omission is the failure to include indicator by predictor interaction terms in the model when required. The present work draws attention to the broader implications of using these models incorrectly, and the difficulties that ensue when they are not built on an appropriate sound statistical framework. Another issue surrounds the inclusion of "tainted" predictor variables that are themselves gender-biased, the most contentious being the (intuitive) choice of rank. Therefore, a brief look at this issue is included; unfortunately, it is shown that rank still today seems to persist as a tainted variable.
Hierarchical clustering for histogram data Billard, L.; Kim, Jaejik
Wiley interdisciplinary reviews. Computational statistics,
September/October 2017, 2017-09-00, 20170901, Letnik:
9, Številka:
5
Journal Article
Recenzirano
Clustering methods for classical data are well established, though the associated algorithms primarily focus on partitioning methods and agglomerative hierarchical methods. With the advent of ...massively large data sets, too large to be analyzed by traditional techniques, new paradigms are needed. Symbolic data methods form one solution to this problem. While symbolic data can be important and arise naturally in their own right, they are particularly relevant when faced with data that emerged from aggregation of (larger) data sets. One format is when the data are histogram‐valued in ℝp, instead of points in ℝp as in classical data. This paper looks at the problem of constructing hierarchies using a divisive polythetic algorithm based on dissimilarity measures derived for histogram observations. WIREs Comput Stat 2017, 9:e1405. doi: 10.1002/wics.1405
This article is categorized under:
Statistical Learning and Exploratory Methods of the Data Sciences > Clustering and Classification
Symbolic data methods form one solution to the problem of massively large data sets emerging from aggregating (larger) data sets. One format is histogram‐valued data in ℝp, instead of classical point data. This article considers constructing hierarchies using a divisive polythetic algorithm based on dissimilarity measures for histogram observations.
This paper introduces a principal component methodology for analysing histogram-valued data under the symbolic data domain. Currently, no comparable method exists for this type of data. The proposed ...method uses a symbolic covariance matrix to determine the principal component space. The resulting observations on principal component space are presented as polytopes for visualization. Numerical representation of the resulting polytopes via histogram-valued output is also presented. The necessary algorithms are included. The technique is illustrated on a weather data set.
Clustering is an explanatory procedure which helps to understand data with complex structure and multivariate relationships, and is a very useful method to extract knowledge and information ...especially from large datasets. When such datasets are aggregated into categories (as driven by scientific questions underlying the analysis), the resulting observations will perforce be expressed as so-called symbolic data (though symbolic data can occur “naturally” in any sized datasets). The focus of this work is to provide a divisive polythetic algorithm to establish clusters for
p
-dimensional histogram-valued data. In addition, two cluster validity indexes for use in establishing the optimal number of clusters are also developed. Finally, the proposed procedure is applied to a large forestry cover type dataset.
Nowadays, most government agencies and local authorities regularly and routinely collect a large amount of data from censuses and surveys and officially publish them for public purposes. The most ...frequently used form for the publication is as statistical tables and it is usually not possible to access the raw data for those tables due to privacy issues. Under these situations, we have to analyze data using only those aggregated tables. These tables typically have formats summarized by ordinal or nominal items. Tables for quantitative variables have histogram-valued formats and those for qualitative variables are represented by multimodal-valued types. Both are classes of the so-called symbolic data. In this study, we propose dissimilarity measures and a divisive clustering algorithm for symbolic multimodal-valued data. In order to split a partition efficiently at each stage, the algorithm extends the monothetic method for binary data. The proposed method is verified by simulation studies and applied to a work-related nonfatal injury and illness dataset.
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