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Histograms in symbolic data analysis
Annals of Operations Research, 1995zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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R Symbolic Data Analysis for Symbolic Artificial Intelligence
Journal of Korean Institute of Intelligent Systems, 2017컴퓨터와 인간은 분명 다르지만 기본적으로 데이터를 저장하고 처리하는 개념적 측면에서는 서로 유사한 구조를 갖는다. 하지만 수집된 전체 데이터를 처리하고 분석하는 컴퓨터와는 달리 인간은 요약된 패턴 단위로 데이터를 처리한다. 즉 인간은 전체 데이터를 다루기보다는 요약된 정보를 통해 최적의 의사결정을 한다. 전체 데이터보다 요약된 정보만을 관리하면 시간과 비용 면에서 더 효율적인 시스템을 구축할 수 있다. 특히 빅데이터 환경에서 인공지능의 학습을 위한 대용량 데이터의 처리 및 분석을 위하여 요약된 정보에 기반 한 데이터학습에 대한 필요성이 제기되고 있다.
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Metrics in Symbolic Data Analysis
2005The Authors consider the general problem of similarity and dissimilarity measures in Symbolic Data Analysis. First they examine the classical definitions of elementary event, assertion object, hierarchical dependences and logical dependences, then they consider some well-known measures resemblance measures between two objects (Sokal-Michener, Roger ...
Luciano Nieddu, Alfredo Rizzi
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Polygonal data analysis: A new framework in symbolic data analysis
Knowledge-Based Systems, 2019Abstract This paper introduces a new framework for polygonal data analysis in the symbolic data analysis paradigm. We show that polygonal data generalizes bivariate interval data. A way for aggregating data in classes is presented to obtain symbolic datasets and, descriptive statistics (for instance, mean, variance, covariance, and histogram) and a ...
Wagner J.F. Silva +2 more
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The Big Picture: Symbolic Data Analysis
CHANCE, 2013I went back to school this past spring semester. To be more precise, I sat in on a special topics course on symbolic data analysis offered by one of my colleagues who is a pioneer in this topic.
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Recursive Partition and Symbolic Data Analysis
1994The theory of symbolic objects is used to define a notion of data that generalizes the one typical of classical data analysis (data-as-matrices). The prediction problem for symbolic objects is defined: it is seen to be a generalization of the prediction problem for classical data. An algorithm of tree-growing is developed for symbolic objects.
A. Ciampi, E. Diday, J. Lebbe, R. Vignes
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Sensor Data Interpretation for Symbolic Analysis
2012This chapter describes an original contribute of this book: a method to solve the correspondence problem in multi-camera systems without the assumption of epipolar geometry. This method is suitable to reduce the sensory gap and the problem of the presence of mutual occlusions among moving objects inside a scene.
Alberto Amato +2 more
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Quality Issues in Symbolic Data Analysis
2007Symbolic Data Analysis is an extension of Classical Data Analysis to more complex data types and tables through the application of certain conditions, where underlying concepts are vital for their further processing. Therefore, the assessment of the quality of Symbolic Data depends extensively on the quality of the collected classical data.
Haralambos Papageorgiou, Maria Vardaki
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Symbolic Data Analysis Applied to Census Data
2008Use of symbolic data analysis to process Italian agricultural census ...
GIUSTI, ANTONIO, GRASSINI, LAURA
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Symbolic missing data imputation in principal component analysis
Statistical Analysis and Data Mining: The ASA Data Science Journal, 2011AbstractThe concept of symbolic data has been developed with the aim of representing variables whose measurement is affected by some internal variation. This idea has been mainly concerned with the need of aggregating individuals in order to summarize large datasets into smaller matrices of manageable size, retaining as much of the original knowledge ...
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