Symbolic Data Analysis

2014 ◽  
Vol 3 (1) ◽  
pp. 1-9
Author(s):  
Sandra Elizabeth González Císaro ◽  
Héctor Oscar Nigro
Keyword(s):  
Data Analysis ◽  
Complex Data ◽  
New Paradigm ◽  
Symbolic Data Analysis ◽  
Standard Data ◽  
Symbolic Data ◽  
The World ◽  
New Type ◽  
Mining Complex ◽  
Internal Variation

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 symbolic object, which is a mathematical model of a concept. In this article the authors are going to present the fundamentals of the symbolic data analysis paradigm and the symbolic object concept. Theoretical aspects and examples allow the authors to understand the SDA paradigm as a tool for mining complex data.

Principles on Symbolic Data Analysis

2009 ◽  
pp. 74-81
Author(s):  
Héctor Oscar Nigro ◽  
Sandra Elizabeth González Císaro
Keyword(s):  
Data Analysis ◽  
Missing Values ◽  
Symbolic Data Analysis ◽  
Human Errors ◽  
Symbolic Data ◽  
Analysis Process ◽  
New Type ◽  
Null Value ◽  
Internal Variation ◽  
Different Sources

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 analysis that allows us represent the complexity of reality, maintaining the internal variation and structure (Diday, 2003). In Data Analysis Process or Data Mining, it is necessary to know the nature of null values - the cases are by absence value, null value or default value -, being also possible and valid to have some imprecision, due to differential semantic in a concept, diverse sources, linguistic imprecision, element resumed in Database, human errors, etc (Chavent, 1997). So, we need a conceptual support to manipulate these types of situations. As we are going to see below, Symbolic Data Analysis (SDA) is a new issue based on a strong conceptual model called Symbolic Object (SO). A “SO” is defined by its “intent” which contains a way to find its “extent”. For instance, the description of habitants in a region and the way of allocating an individual to this region is called “intent”, the set of individuals, which satisfies this intent, is called “extent” (Diday 2003). For this type of analysis, different experts are needed, each one giving their concepts.

Symbolic Objects and Symbolic Data Analysis

2005 ◽  
pp. 665-670 ◽  
Author(s):  
Héctor Oscar Nigro ◽  
Sandra Elizabeth González Císaro
Keyword(s):  
Data Analysis ◽  
Missing Values ◽  
Symbolic Data Analysis ◽  
Symbolic Data ◽  
New Type ◽  
Internal Variation ◽  
Different Sources

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).

Symbolic Data Clustering

2011 ◽  
pp. 1087-1091 ◽  
Author(s):  
Edwin Diday ◽  
M. Narasimha Murthy
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Internal Structure ◽  
Data Clustering ◽  
Large Datasets ◽  
Complex Data ◽  
Symbolic Data Analysis ◽  
New Techniques ◽  
Symbolic Data

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. Such a view permits us to deal with data at a conceptual level, and as a consequence, SDA is ideally suited for data mining. Symbolic data have their own internal structure that necessitates the need for new techniques that generally differ from the ones used on conventional data (Billard & Diday, 2003). Clustering generates abstractions that can be used in a variety of decision-making applications (Jain, Murty, & Flynn, 1999). In this article, we deal with the application of clustering to SDA.

scholarly journals Fraud Detection in Healthcare System using Symbolic Data Analysis

2021 ◽  
Vol 10 (9) ◽  
pp. 1-7
Author(s):  
Sahana Munavalli ◽  
◽  
Sanjeevakumar M. Hatture ◽  
Keyword(s):  
Data Mining ◽  
Health Insurance ◽  
Data Analysis ◽  
Claim Data ◽  
Symbolic Data Analysis ◽  
Standard Data ◽  
Symbolic Data ◽  
Large Sets ◽  
Strenuous Work ◽  
Insurance Claim Data

In the era of digitization the frauds are found in all categories of health insurance. It is finished next to deliberate trickiness or distortion for acquiring some pitiful advantage in the form of health expenditures. Bigdata analysis can be utilized to recognize fraud in large sets of insurance claim data. In light of a couple of cases that are known or suspected to be false, the anomaly detection technique computes the closeness of each record to be fake by investigating the previous insurance claims. The investigators would then be able to have a nearer examination for the cases that have been set apart by data mining programming. One of the issues is the abuse of the medical insurance systems. Manual detection of frauds in the healthcare industry is strenuous work. Fraud and Abuse in the Health care system have become a significant concern and that too inside health insurance organizations, from the most recent couple of years because of the expanding misfortunes in incomes, handling medical claims have become a debilitating manual assignment, which is done by a couple of clinical specialists who have the duty of endorsing, adjusting, or dismissing the appropriations mentioned inside a restricted period from their gathering. Standard data mining techniques at this point do not sufficiently address the intricacy of the world. In this way, utilizing Symbolic Data Analysis is another sort of data analysis that permits us to address the intricacy of the real world and to recognize misrepresentation in the dataset.

Kernel Generative Topographic Mapping of Protein Sequences

2012 ◽  
pp. 195-208
Author(s):  
M.I. Cardenas ◽  
A. Vellido ◽  
I. Olier ◽  
X. Rovira ◽  
J. Giraldo
Keyword(s):  
Data Analysis ◽  
Kernel Method ◽  
Protein Sequences ◽  
Topographic Mapping ◽  
Complex Data ◽  
Generative Topographic Mapping ◽  
Protein Amino Acid ◽  
Genomics And Proteomics ◽  
The World ◽  
Symbolic Sequences

The world of pharmacology is becoming increasingly dependent on the advances in the fields of genomics and proteomics. The –omics sciences bring about the challenge of how to deal with the large amounts of complex data they generate from an intelligence data analysis perspective. In this chapter, the authors focus on the analysis of a specific type of proteins, the G protein-couple receptors, which are the target for over 15% of current drugs. They describe a kernel method of the manifold learning family for the analysis of protein amino acid symbolic sequences. This method sheds light on the structure of protein subfamilies, while providing an intuitive visualization of such structure.

Sign in / Sign up

Export Citation Format

Share Document