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About 10,000 papers for “Symbolic data analysis”

Symbolic Objects and Symbolic Data Analysis

Héctor Oscar Nigro, Sandra Elizabeth González Císaro · IGI Global eBooks · 2011

Today’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, 2003).

Cited by 0

6. Symbolic Data Analysis

Manabu Ichino · Journal of the Japanese Society of Computational Statistics · 2003

This paper presents new neighborhood graphs useful to solve feature selection problems in pattern recognition for symbolic data. In pattern recognition for symbolic data, each sample pattern is described not only by quantitative features but also by qualitative features. We introduce the Cartesian System Model (CSM) as a mathematical model to treat symbolic data. Then, we define the Generality Ordered Mutual Neighborhood Graph and the Generality Ordered Interclass Mutual Neighborhood Graph based on the CSM. These neighborhood graphs play central roles in seeing details of the interclass structures. We outline the basic idea of our classifier by using simple examples.

Cited by 1Open access

Symbolic Analysis of Financial Data

Goupil Touati Diday, F. Goupil, M. Touati et al. · 2000

We have as input two portfolio of stocks managed by ING bank (North Holland). Each portfolio and each stock is described by several variables every open day of a week and during eight weeks. A portfolio is characterised by variables as its value, its performance, a stock is characterised by variable as its value, its number in the portfolio and its performance. In this paper, we study a symbolic data table where each row represents one of the eight weeks and each column represents a variable which value is an interval [min, max] corresponding to the minimum and maximum values taken during this week. Hence each cell of the data table contains an interval instead a number as usual. The SODAS software aim to extend standard data analysis to more complex data like financial data. In this paper we use it in order to study the performance of two portfolio in relation with their stocks. It results from this study that Sodas is able to study the internal variation of classes (here, the weeks) by giving explanations and graphics. 1

Cited by 0

symbolicDA: Analysis of Symbolic Data

Andrzej Dudek, Marcin Pelka, Justyna Wilk et al. · 2013

Symbolic data analysis methods: importing/exporting data from ASSO XML Files, distance calculation for symbolic data (Ichino-Yaguchi, de Carvalho measure), zoom star plot, 3d interval plot, multidimensional scaling for symbolic interval data, dynamic clustering based on distance matrix, HINoV method for symbolic data, Ichino's feature selection method, principal component analysis for symbolic interval data, decision trees for symbolic data based on optimal split with bagging, boosting and random forest approach (+visualization), kernel discriminant analysis for symbolic data, Kohonen's self-organizing maps for symbolic data, replication and profiling, artificial symbolic data generation. (Milligan, G.W., Cooper, M.C. (1985) &lt;<a href="https://doi.org/10.1007%2FBF02294245" target="_top">doi:10.1007/BF02294245</a>&gt;, Breiman, L. (1996), &lt;<a href="https://doi.org/10.1007%2FBF00058655" target="_top">doi:10.1007/BF00058655</a>&gt;, Hubert, L., Arabie, P. (1985), &lt;<a href="https://doi.org/10.1007%2FBF01908075" target="_top">doi:10.1007%2FBF01908075</a>&gt;, Ichino, M., &amp; Yaguchi, H. (1994), &lt;<a href="https://doi.org/10.1109%2F21.286391" target="_top">doi:10.1109/21.286391</a>&gt;, Rand, W.M. (1971) &lt;<a href="https://doi.org/10.1080%2F01621459.1971.10482356" target="_top">doi:10.1080/01621459.1971.10482356</a>&gt;, Breckenridge, J.N. (2000) &lt;<a href="https://doi.org/10.1207%2FS15327906MBR3502_5" target="_top">doi:10.1207/S15327906MBR3502_5</a>&gt;, Groenen, P.J.F, Winsberg, S., Rodriguez, O., Diday, E. (2006) &lt;<a href="https://doi.org/10.1016%2Fj.csda.2006.04.003" target="_top">doi:10.1016/j.csda.2006.04.003</a>&gt;, Dudek, A. (2007), &lt;<a href="https://doi.org/10.1007%2F978-3-540-70981-7_4" target="_top">doi:10.1007/978-3-540-70981-7_4</a>&gt;).

Cited by 1Open access

Symbolic Analysis of Financial Data

Françoise Goupil, M. Touati, Edwin Diday et al. · 2000

We have as input two portfolio of stocks managed by ING bank (North Holland). Each portfolio and each stock is described by several variables every open day of a week and during eight weeks. A portfolio is characterised by variables as its value, its performance, a stock is characterised by variable as its value, its number in the portfolio and its performance. In this paper, we study a symbolic data table where each row represents one of the eight weeks and each column represents a variable which value is an interval [min, max] corresponding to the minimum and maximum values taken during this week. Hence each cell of the data table contains an interval instead a number as usual. The SODAS software aim to extend standard data analysis to more complex data like financial data. In this paper we use it in order to study the performance of two portfolio in relation with their stocks. It results from this study that Sodas is able to study the internal variation of classes (here, the weeks) by giving explanations and graphics.

Cited by 0

Symbolic Data Analysis: Definition and Examples

Lynne Billard, Edwin Diday · Base Institutionnelle de Recherche de l'université Paris-Dauphine (BIRD) (University Paris-Dauphine) · 2011

With the advent of computers, large, very large datasets have become routine. What&#13;\nis not so routine is how to analyse these data and/or how to glean useful information&#13;\nfrom within their massive confines. One approach is to summarize large data sets in such&#13;\na way that the resulting summary dataset is of a manageable size. One consequence&#13;\nof this is that the data may no longer be formatted as single values such as is the&#13;\ncase for classical data, but may be represented by lists, intervals, distributions and&#13;\nthe like. These summarized data are examples of symbolic data. This paper looks&#13;\nat the concept of symbolic data in general, and then attempts to review the methods&#13;\ncurrently available to analyse such data. It quickly becomes clear that the range of&#13;\nmethodologies available draws analogies with developments prior to 1900 which formed&#13;\na foundation for the inferential statistics of the 1900’s, methods that are largely limited&#13;\nto small (by comparison) data sets and limited to classical data formats. The scarcity of&#13;\navailable methodologies for symbolic data also becomes clear and so draws attention to&#13;\nan enormous need for the development of a vast catalogue (so to speak) of new symbolic&#13;\nmethodologies along with rigorous mathematical foundational work for these methods.

Cited by 14

Proximity measures in symbolic data analysis

BOCCI, Laura · IRIS Research product catalog (Sapienza University of Rome) · 2002

The aim of this paper consists in showing the building of dissimilarity measures between either Boolean and probabilistic symbolic objects. The paper deals with an analytical overview of the existing dissimilarity measures between Boolean symbolic objects. Also a new approach on calculating dissimilarity measure between probabilistic symbolic objects is presented.

Cited by 0