1,720,969 research outputs found
Scaling data mining activities on very large datasets
No full textThis thesis addresses the issue of enhancing the scalability of data mining techniques, with specific emphasis on association rule and frequent itemset mining. In particular, it proposes a scalable itemset mining approach relying on (i) a persistent (disk-based) representation of the transactional data, (ii) ad-hoc data retrieval techniques, and (iii)~strategies for the integration of existing itemset mining algorithms. A parallel design based on the same approach, to perform itemset extraction in a parallel and/or distributed environment, is also described. To address the manageability of frequent itemsets, a concise disk-based representation, with a set of querying techniques, is proposed. This work has been preliminarly validated in the Semantic Web domain, to identify semantic relationships from textual collections with a semi-automatic approach. As a minor topic, the extracion of frequent itemsets from streams of data, modelled as a set of transactional data windows, has also been tackled by proposing an online/offline analysis approach. Finally, a software platform, developed in a joint effort with the Institute for Cancer Research and Treatment (Candiolo), is presented, allowing the collection, the integration, and the analysis of heterogeneous data from the molecular oncology fiel
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
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
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
Scalable out-of-core itemset mining
No full textItemset mining looks for correlations among data items in large transactional
datasets. Traditional in-core mining algorithms do not scale well with
huge data volumes, and are hindered by critical issues such as long execution
times due to massive memory swap and main-memory exhaustion. This work
is aimed at overcoming the scalability issues of existing in-core algorithms
by improving their memory usage. A persistent structure, VLDBMine, to
compactly store huge transactional datasets on disk and efficiently support
large-scale itemset mining is proposed. VLDBMine provides a compact and
complete representation of the data, by exploiting two different data structures
suitable for diverse data distributions, and includes an appropriate
indexing structure, allowing selective data retrieval. Experimental validation,
performed on both real and synthetic datasets, shows the compactness
of the VLDBMine data structure and the efficiency and scalability on large
datasets of the mining algorithms supported by it
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