1,720,955 research outputs found
Data mining through data visualization: a case study on predicting churners on telecomunications data set
Full text linkBaşarslan, Muhammet Sinan (Dogus Author)Data mining is the process of extracting meaningful information from a large, raw data. These processes are carried out by various, detailed methods. And, the obtained results are used to make various interpretations and to draw conclusions. Deductions can either be made by interpreting the data after various operations or by plotting the data in various forms of graphs. This type of interpretation over graphics is called data mining through data visualization. Generating graphs that can be used to draw various conclusions on a telecommunications data set with the help of some packages included in the R program is presented in the paper. It does not require upper-level math skills to interpret these graphics; and everyone having knowledge about the industry and data set of the graphs has the ability to plot similar graphs and make analysis and interpretations regarding the results obtained on the data set at hand. In this study, R language was preferred as the software infrastructure for data mining applications, and graphs were plotted for interpretation through data visualization with data mining
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe 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
Performance Analysis Of Fuzzy Rough Set-Based And Correlation-Based Attribute Selection Methods On Detection Of Chronic Kidney Disease With Various Classifiers
Full text linkInternational Scientific Meeting on Electrical-Electronics and Biomedical Engineering and Computer Science (EBBT) -- APR 24-26, 2019 -- Istanbul Arel Univ, Kemal Gozukara Campus, Istanbul, TURKEYBASARSLAN, MUHAMMET SINAN/0000-0002-7996-9169WOS: 000491430200016Technological developments generally have positive effects on our daily lives especially on health domain. Diagnosing diseases through new machines or methods are easier than compared to the past. Benchmarking the effect of attribute selection methods on the performance of classification algorithms in a study to diganose the chronic kidney disease (CKD) by using classification algorithms are aimed. Data set on CKD taken from the UCI machine learning repository has been used for the experiments. After a variety of pre-processing, normalization and attribute selection processes, classifier models are designed. In order to determine the attributes that have gerater contribution on the classification results, the Correlation Based attribute selection (CBAS) method and Fuzzy Rough Set Based attribute selection (FRSBAS) method were used. Two data sets obtained by each attribute selection method and the raw data are classified by 4 classifiers including k-Nearest Neighbor, Navie Bayes, Random Forest and Logistic Regression. The test and training data are separated by 5-fold cross validation. The accuracy, precision, sensitivity, ROC curve and F-measure parameters obtained from confusion matrix are used to compare and evaluate the results of the models. As a result of the study, it is seen that the application of FRSBAS method on CKD data set performs better in all classification algorithms.IEEE Turkey Sect, IEEE EMB, Erasmus+, Europas
Variations on the Author
Full text link“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
Full text linkWe 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
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe 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
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