1,720,973 research outputs found
Optimizing Euclidean Distance Computation
No full textThis paper presents a comparative analysis of seventeen different approaches to optimizing Euclidean distance computations, which is a core mathematical operation that plays a critical role in a wide range of algorithms, particularly in machine learning and data analysis. The Euclidean distance, being a computational bottleneck in large-scale optimization problems, requires efficient computation techniques to improve the performance of various distance-dependent algorithms. To address this, several optimization strategies can be employed to accelerate distance computations. From spatial data structures and approximate nearest neighbor algorithms to dimensionality reduction, vectorization, and parallel computing, various approaches exist to accelerate Euclidean distance computation in different contexts. Such approaches are particularly important for speeding up key machine learning algorithms like K-means and K-nearest neighbors (KNNs). By understanding the trade-offs and assessing the effectiveness, complexity, and scalability of various optimization techniques, our findings help practitioners choose the most appropriate methods for improving Euclidean distance computations in specific contexts. These optimizations enable scalable and efficient processing for modern data-driven tasks, directly leading to reduced energy consumption and a minimized environmental impact
Variable Landscape Search: A Novel Metaheuristic Paradigm for Unlocking Hidden Dimensions in Global Optimization
No full textThis paper presents the Variable Landscape Search (VLS), a novel metaheuristic designed for global optimization of complex problems by dynamically altering the objective function landscape. Unlike traditional methods that operate within a static search space, VLS introduces an additional level of flexibility and diversity to the global optimization process. It does this by continuously and iteratively varying the objective function landscape through slight modifications to the problem formulation, the input data, or both. The innovation of the VLS metaheuristic stems from its unique capability to seamlessly fuse dynamic adaptations in problem formulation with modifications in input data. This dual-modality approach enables continuous exploration of interconnected and evolving search spaces, significantly enhancing the potential for discovering optimal solutions in complex, multi-faceted optimization scenarios, thereby making it adaptable across various domains. In this paper, one of the theoretical results is obtained as a generalization of three alternative metaheuristics, which have been reduced to special cases of VLS: Variable Formulation Search (VFS), Formulation Space Search (FSS), and Variable Search Space (VSS). As a practical application, the paper demonstrates the superior efficiency of a recent big data clustering algorithm through its conceptualization using the VLS paradigm
BiModalClust: Fused Data and Neighborhood Variation for Advanced K-Means Big Data Clustering
No full textK-means clustering is a fundamental tool in data mining, yet its scalability and efficacy decline when faced with massive datasets. In this work, we introduce BiModalClust, a novel clustering algorithm that leverages a bimodal optimization paradigm to overcome these challenges. Our approach simultaneously optimizes two interdependent modalities: the input data stream and the neighborhood structure of the solution landscape, which emerges from iterative restrictions of the Minimum Sum-of-Squares Clustering (MSSC) objective function to sampled subsets of the data. By integrating the Variable Neighborhood Search (VNS) metaheuristic, we systematically explore and refine these landscapes through dynamic reinitialization of degenerate centroids and adaptive exploration of expanding neighborhoods. This dual-stream optimization not only transforms traditional local search into a more global and robust process but also ensures computational scalability and precision. Extensive experimentation on diverse real-world datasets demonstrates that BiModalClust achieves superior clustering performance among K-means-based methods in big data environments
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
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
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