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International Journal of Signs and Semiotic Systems, 2014
Standard data mining techniques no longer adequately represent the complexity of the world. So, a new paradigm is necessary. Symbolic Data Analysis is a new type of data analysis that allows us to represent the complexity of reality, maintaining the internal variation and structure developed by Diday (2003). This new paradigm is based on the concept of
Sandra Elizabeth González Císaro +1 more
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Standard data mining techniques no longer adequately represent the complexity of the world. So, a new paradigm is necessary. Symbolic Data Analysis is a new type of data analysis that allows us to represent the complexity of reality, maintaining the internal variation and structure developed by Diday (2003). This new paradigm is based on the concept of
Sandra Elizabeth González Císaro +1 more
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Symbolic representation of hyperspectral data
Applied Optics, 1987We have developed a symbolic representation of hyperspectral data using the scale space techniques of Witkin. We created a scale space image of hyperspectral data from convolution with Gaussian masks and then a fingerprint that extracts individual features from the original data.
M A, Piech, K R, Piech
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2005
In data mining, we generate class/cluster models from large datasets. Symbolic Data Analysis (SDA) is a powerful tool that permits dealing with complex data (Diday, 1988) where a combination of variables and logical and hierarchical relationships among them are used.
Murthy, Narasimha, Diday, Edwin
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In data mining, we generate class/cluster models from large datasets. Symbolic Data Analysis (SDA) is a powerful tool that permits dealing with complex data (Diday, 1988) where a combination of variables and logical and hierarchical relationships among them are used.
Murthy, Narasimha, Diday, Edwin
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Symbolic Objects and Symbolic Data Analysis
2005Today’s technology allows storing vast quantities of information from different sources in nature. This information has missing values, nulls, internal variation, taxonomies, and rules. We need a new type of data that allow us to represent the complexity of reality, maintaining the internal variation and structure (Bock & Diday, 2000; Diday, 2002 ...
Héctor Oscar Nigro +1 more
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Clustering constrained symbolic data
Pattern Recognition Letters, 2009Dealing with multi-valued data has become quite common in both the framework of databases as well as data analysis. Such data can be constrained by domain knowledge provided by relations between the variables and these relations are expressed by rules.
De A. T. De Carvalho, Francisco +2 more
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Symbolic substitution system for data compression
Applied Optics, 1993A new application of symbolic substitution is presented for string matching with the goal of data compression. A temporal sequence of input symbols is mapped onto a two-dimensional array that contains a tree structure, which in turn is mapped into another array for string generation.
S D, Goodman, M A, Brooke
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Symbol Timing Recovery for CPM with Correlated Data Symbols
IEEE Transactions on Communications, 2009We consider symbol timing recovery for continuous phase modulations (CPMs) with correlated data symbols. A popular example of such a scheme is shaped offset quadrature phase-shift keying (SOQPSK). We propose an extension to an existing non-data-aided (blind) timing error detector (TED) to make it compatible with such modulation schemes.
Prashanth Chandran, Erik Perrins
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Usual operations with symbolic data under normal symbolic form
Applied Stochastic Models in Business and Industry, 1999Symbolic objects provide a good and efficient way to summarize a large amount of information in a single object. Symbolic objects are represented by symbolic tables, like usual statistical tables, in the form of rows and columns, the former representing individual (in this case a symbolic object), the latter representing variables.
Csernel, Marc +1 more
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Normalization of interval symbolic data
2009 16th International Conference on Industrial Engineering and Engineering Management, 2009As a new tool in data mining, symbolic data analysis (SDA) can not only decrease the computational complexity of huge data sets, but also master the property of the sample integrally. In many statistical analysis methods the sample data need to be normalized in advance. This paper focuses on the normalization of interval symbolic data.
Junpeng Guo, Wenhua Li, Sue Cheng
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