Symbolic Data Mining in Musicology

2011 ◽  
pp. 351-371
Keyword(s):  
Data Mining ◽  
Symbolic Data

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.

Towards Symbolic Data Mining in Numerical Time Series

2007 ◽  
pp. 231-243
Author(s):  
Agustín Santamaría ◽  
África López-Illescas ◽  
Aurora Perez-Perez ◽  
Juan P. Caraça-Valente
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Symbolic Data

Knowledge Discovery in Databases with Diversity of Data Types

2011 ◽  
pp. 1117-1123 ◽  
Author(s):  
QingXiang Wu ◽  
Martin McGinnity ◽  
Girijesh Prasad ◽  
David Bell
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Knowledge Discovery ◽  
Set Theory ◽  
Missing Values ◽  
Numerical Data ◽  
Mixed Data ◽  
Data Types ◽  
Useful Knowledge ◽  
Symbolic Data

Data mining and knowledge discovery aim at finding useful information from typically massive collections of data, and then extracting useful knowledge from the information. To date a large number of approaches have been proposed to find useful information and discover useful knowledge; for example, decision trees, Bayesian belief networks, evidence theory, rough set theory, fuzzy set theory, kNN (k-nearest-neighborhood) classifier, neural networks, and support vector machines. However, these approaches are based on a specific data type. In the real world, an intelligent system often encounters mixed data types, incomplete information (missing values), and imprecise information (fuzzy conditions). In the UCI (University of California – Irvine) Machine Learning Repository, it can be seen that there are many real world data sets with missing values and mixed data types. It is a challenge to enable machine learning or data mining approaches to deal with mixed data types (Ching, 1995; Coppock, 2003) because there are difficulties in finding a measure of similarity between objects with mixed data type attributes. The problem with mixed data types is a long-standing issue faced in data mining. The emerging techniques targeted at this issue can be classified into three classes as follows: (1) Symbolic data mining approaches plus different discretizers (e.g., Dougherty et al., 1995; Wu, 1996; Kurgan et al., 2004; Diday, 2004; Darmont et al., 2006; Wu et al., 2007) for transformation from continuous data to symbolic data; (2) Numerical data mining approaches plus transformation from symbolic data to numerical data (e.g.,, Kasabov, 2003; Darmont et al., 2006; Hadzic et al., 2007); (3) Hybrid of symbolic data mining approaches and numerical data mining approaches (e.g.,, Tung, 2002; Kasabov, 2003; Leng et al., 2005; Wu et al., 2006). Since hybrid approaches have the potential to exploit the advantages from both symbolic data mining and numerical data mining approaches, this chapter, after discassing the merits and shortcomings of current approaches, focuses on applying Self-Organizing Computing Network Model to construct a hybrid system to solve the problems of knowledge discovery from databases with a diversity of data types. Future trends for data mining on mixed type data are then discussed. Finally a conclusion is presented.

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