Determination of the representativity between two multidimensional data sets by a comparison of their structure

1998 ◽  
Vol 40 (2) ◽  
pp. 129-144 ◽  
Author(s):  
Delphine Jouan-Rimbaud ◽  
D.L. Massart ◽  
C.A. Saby ◽  
C. Puel
Keyword(s):  
Multidimensional Data ◽  
Data Sets ◽  
Multidimensional Data Sets

scholarly journals Geo-scape, a granularity depended spatialization tool for visualizing multidimensional data sets

2010 ◽  
Vol 13 (4) ◽  
pp. 275-284
Author(s):  
Kontaxaki Sofia ◽  
Kokla Margarita ◽  
Kavouras Marinos
Keyword(s):  
Multidimensional Data ◽  
Data Sets ◽  
Multidimensional Data Sets

scholarly journals A structured approach to predictive modeling of a two-class problem using multidimensional data sets

Methods ◽  
2013 ◽  
Vol 61 (1) ◽  
pp. 73-85 ◽  
Author(s):  
Heidi Spratt ◽  
Hyunsu Ju ◽  
Allan R. Brasier
Keyword(s):  
Predictive Modeling ◽  
Multidimensional Data ◽  
Data Sets ◽  
Multidimensional Data Sets ◽  
Structured Approach

scholarly journals Variable selection study using Procrustes analysis

2013 ◽  
Vol 1 (1) ◽  
pp. 7 ◽  
Author(s):  
Casimiro S. Munita ◽  
Lúcia P. Barroso ◽  
Paulo M.S. Oliveira
Keyword(s):  
Analytical Techniques ◽  
Procrustes Analysis ◽  
Multidimensional Data ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Data Set ◽  
Essential Information ◽  
Selection Of Variables ◽  
Archaeological Ceramic ◽  
Multidimensional Data Sets

Several analytical techniques are often used in archaeometric studies, and when used in combination, these techniques can be used to assess 30 or more elements. Multivariate statistical methods are frequently used to interpret archaeometric data, but their applications can be problematic or difficult to interpret due to the large number of variables. In general, the analyst first measures several variables, many of which may be found to be uninformative, this is naturally very time consuming and expensive. In subsequent studies the analyst may wish to measure fewer variables while attempting to minimize the loss of essential information. Such multidimensional data sets must be closely examined to draw useful information. This paper aims to describe and illustrate a stopping rule for the identification of redundant variables, and the selection of variables subsets, preserving multivariate data structure using Procrustes analysis, selecting those variables that are in some senses adequate for discrimination purposes. We provide an illustrative example of the procedure using a data set of 40 samples in which were determined the concentration of As, Ce, Cr, Eu, Fe, Hf, La, Na, Nd, Sc, Sm, Th, and U obtained via instrumental neutron activation analysis (INAA) on archaeological ceramic samples. The results showed that for this data set, only eight variables (As, Cr, Fe, Hf, La, Nd, Sm, and Th) are required to interpret the data without substantial loss information.

Imaging, Visualization, and Computation in Developmental Biology

2019 ◽  
Vol 2 (1) ◽  
pp. 223-251 ◽  
Author(s):  
Francesco Cutrale ◽  
Scott E. Fraser ◽  
Le A. Trinh
Keyword(s):  
Data Science ◽  
Molecular Mechanisms ◽  
Cellular Tissue ◽  
Multidimensional Data ◽  
Data Sets ◽  
Cellular Events ◽  
Intact Embryo ◽  
In Vivo Labeling ◽  
Multidimensional Data Sets

Embryonic development is highly complex and dynamic, requiring the coordination of numerous molecular and cellular events at precise times and places. Advances in imaging technology have made it possible to follow developmental processes at cellular, tissue, and organ levels over time as they take place in the intact embryo. Parallel innovations of in vivo probes permit imaging to report on molecular, physiological, and anatomical events of embryogenesis, but the resulting multidimensional data sets pose significant challenges for extracting knowledge. In this review, we discuss recent and emerging advances in imaging technologies, in vivo labeling, and data processing that offer the greatest potential for jointly deciphering the intricate cellular dynamics and the underlying molecular mechanisms. Our discussion of the emerging area of “image-omics” highlights both the challenges of data analysis and the promise of more fully embracing computation and data science for rapidly advancing our understanding of biology.

scholarly journals Relevant Independent Variables on MOBA Video Games to Train Machine Learning Algorithms

2021 ◽  
Author(s):  
Juan Guillermo López Guzmán ◽  
Cesar Julio Bustacara Medina
Keyword(s):  
Machine Learning ◽  
Video Games ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Multidimensional Data ◽  
Data Sets ◽  
Network Architectures ◽  
Independent Variables ◽  
Learning Techniques ◽  
Multidimensional Data Sets

Popularity of Multiplayer Online Battle Arena (MOBA) video games has grown considerably, its popularity as well as the complexity of their playability, have attracted the attention in recent years of researchers from various areas of knowledge and in particular how they have resorted to different machine learning techniques. The papers reviewed mainly look for patterns in multidimensional data sets. Furthermore, these previous researches do not present a way to select the independent variables (predictors) to train the models. For this reason, this paper proposes a list of variables based on the techniques used and the objectives of the research. It allows to provide a set of variables to find patterns applied in MOBA videogames. In order to get the mentioned list, the consulted works were grouped by the used machine learning techniques, ranging from rule-based systems to complex neural network architectures. Also, a grouping technique is applied based on the objective of each research proposed.

scholarly journals Multidimensional Arrays and the nArray Package

2007 ◽  
Author(s):  
Ofri Sadowsky ◽  
Daniel Li ◽  
Anton Deguet ◽  
Peter Kazanzides
Keyword(s):  
Multidimensional Data ◽  
Data Sets ◽  
Unified Framework ◽  
Multidimensional Arrays ◽  
Engineering Research ◽  
The Core ◽  
Comparable Performance ◽  
Core Elements ◽  
Volume Algorithm ◽  
Multidimensional Data Sets

At the Johns Hopkins University’s Engineering Research Center for Computer-Integrated Surgical Systems and Technology (ERC-CISST) laboratory, we have designed and developed a platformindependent C++ software package, called the nArray library, that provides a unified framework for efficiently working with multidimensional data sets. In this paper, we present and discuss the core elements of the library, including its intuitive and uniform API, efficient arithmetic engine algorithm, and efficient sub-volume algorithm. We then compare the performance of the nArray library with that of an existing multidimensional array toolkit, ITK. We conclude that the nArray library is more efficient than ITK in many situations, especially in operations on sub-arrays, and that the two packages have comparable performance in many other scenarios. The underlying algorithms, if incorporated in ITK, can help improve its performance.

OLAP Visualization

2011 ◽  
pp. 1439-1446 ◽  
Author(s):  
Alfredo Cuzzocrea ◽  
Svetlana Mansmann
Keyword(s):  
Computer Science ◽  
High Dimensional Data ◽  
Real Life ◽  
Science Research ◽  
Research Field ◽  
Multidimensional Data ◽  
High Dimensional ◽  
Data Sets ◽  
Visualization Of Data ◽  
Multidimensional Data Sets

The problem of efficiently visualizing multidimensional data sets produced by scientific and statistical tasks/ processes is becoming increasingly challenging, and is attracting the attention of a wide multidisciplinary community of researchers and practitioners. Basically, this problem consists in visualizing multidimensional data sets by capturing the dimensionality of data, which is the most difficult aspect to be considered. Human analysts interacting with high-dimensional data often experience disorientation and cognitive overload. Analysis of high- dimensional data is a challenge encountered in a wide set of real-life applications such as (i) biological databases storing massive gene and protein data sets, (ii) real-time monitoring systems accumulating data sets produced by multiple, multi-rate streaming sources, (iii) advanced Business Intelligence (BI) systems collecting business data for decision making purposes etc. Traditional DBMS front-end tools, which are usually tuple-bag-oriented, are completely inadequate to fulfill the requirements posed by an interactive exploration of high-dimensional data sets due to two major reasons: (i) DBMS implement the OLTP paradigm, which is optimized for transaction processing and deliberately neglects the dimensionality of data; (ii) DBMS operators are very poor and offer nothing beyond the capability of conventional SQL statements, what makes such tools very inefficient with respect to the goal of visualizing and, above all, interacting with multidimensional data sets embedding a large number of dimensions. Despite the above-highlighted practical relevance of the problem of visualizing multidimensional data sets, the literature in this field is rather scarce, due to the fact that, for many years, this problem has been of relevance for life science research communities only, and interaction of the latter with the computer science research community has been insufficient. Following the enormous growth of scientific disciplines like Bio-Informatics, this problem has then become a fundamental field in the computer science academic as well as industrial research. At the same time, a number of proposals dealing with the multidimensional data visualization problem appeared in literature, with the amenity of stimulating novel and exciting application fields such as the visualization of Data Mining results generated by challenging techniques like clustering and association rule discovery. The above-mentioned issues are meant to facilitate understanding of the high relevance and attractiveness of the problem of visualizing multidimensional data sets at present and in the future, with challenging research findings accompanied by significant spin-offs in the Information Technology (IT) industrial field. A possible solution to tackle this problem is represented by well-known OLAP techniques (Codd et al., 1993; Chaudhuri & Dayal, 1997; Gray et al., 1997), focused on obtaining very efficient representations of multidimensional data sets, called data cubes, thus leading to the research field which is known in literature under the terms OLAP Visualization and Visual OLAP, which, in the remaining part of the article, are used interchangeably.

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