1,720,966 research outputs found
Direct local pattern sampling by efficient two-step random procedures
No full textWe present several exact and highly scalable local pattern sampling algorithms. They can be used as an alternative to exhaustive local pattern discovery methods (e.g, frequent set mining or optimistic-estimator-based subgroup discovery) and can substantially improve efficiency as well as controllability of pattern discovery processes. While previous sampling approaches mainly rely on theMarkov chainMonte Carlo method, our procedures are direct, i.e., non processsimulating, sampling algorithms. The advantages of these direct methods are an almost optimal time complexity per pattern as well as an exactly controlled distribution of the produced patterns. Namely, the proposed algorithms can sample (item-)sets according to frequency, area, squared frequency, and a class discriminativity measure. Experiments demonstrate that these procedures can improve the accuracy of pattern-based models similar to frequent sets and often also lead to substantial gains in terms of scalability. Copyright 2011 ACM
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Intuitive Exploration of Multivariate Data
Get PDFApproaching a dataset with an analysis question is usually not a trivial process. Apart from integrating, cleaning and pre-processing the data, typical issues are to generate and validate hypotheses, to understand which algorithms to apply, to estimate parameter settings and to interpret intermediate analysis results. To this end, it is often helpful to explore the data at first in order to find and understand its main characteristics, the driving influences, structures and relations among the data records, as well as revealing outliers. Exploratory data analysis, a term coined by John W. Tukey (Tukey, 1977), is a loose set of methods, mostly of graphical nature, to summarize and understand the main characteristics of the data at hand. This work extends the set of exploratory data analysis methods by proposing several new methods that support the analyst in his, or her task of understanding the data. Over the course of this thesis, two conceptually different approaches are investigated. The first approach studies pattern mining algorithms, a family of methods that find and report hypotheses which describe interesting sub-populations of the dataset to the analyst, where the interestingness is measured by different quality functions. As the results of pattern mining methods are interpretable by a human expert, these algorithms are often utilized to study a dataset in an exploratory way. Note that many pattern mining algorithms address the problem of finding a small set of diverse high patterns. To this end, this work introduces two new algorithms, one for relevant and one for Δ-relevant subgroup discovery. In addition an algorithmic framework for sampling patterns according to different pattern quality measures is introduced. The second approach towards exploratory data analysis leaves the discovery of interesting sub-populations to the analyst and enables him, or her to study a two dimensional projection of the data and interact with it. A scatter plot visualization of the projected data lets the analyst observe the data collection as a whole and visually uncover interesting structures. Manipulating the locations of individual data records within the plot further enables the analyst to alter the projection angle and to actively steer the projection. This way relations among the data records can be set, or discovered and aspects of the data’s underlying distribution can be explored in a visual manner. Finding the according projections is not trivial and throughout this thesis three novel approaches are proposed to do so. The thesis concludes with a synthesis of both approaches. Classical pattern mining algorithms often aim at reducing the output of patterns to a small set of highly interesting and diverse patterns. However, by discarding most of the patterns, a trade-off has to be made between ruling out potentially insightful patterns and possibly drowning the analyst in results. Combining interactive visual exploration techniques with pattern discovery, on the other hand, excels on working with larger pattern collections, as the underlying pattern-distribution emerges more clearly. This way, the analyst does not only retain an overview on the underlying structure of the dataset, but can also survey the relations among the interesting aspects of the dataset
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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