Advances in Processing, Mining, and Learning Complex Data: From Foundations to Real-World Applications

Foundations and Advances in Data Mining

This chapter describes systems that automatically classify web pages intomeaningful categories. It first defines two types of web page classification:subject based and genre based classifications. It then describes the state ofthe art techniques and subsystems used to build automatic web pageclassification systems, including web page representations, dimensionalityreductions...

Analysis of Pre-processing and Post-processing Methods and Using Data Mining to Diagnose Heart Diseases

Today, a great deal of data is generated in the medical field. Acquiring useful knowledge from this raw data requires data processing and detection of meaningful patterns and this objective can be achieved through data mining. Using data mining to diagnose and prognose heart diseases has become one of the areas of interest for researchers in recent years. In this study, the literature on the ap...

Enhancing Learning from Imbalanced Classes via Data Preprocessing: A Data-Driven Application in Metabolomics Data Mining

This paper presents a data mining application in metabolomics. It aims at building an enhanced machine learning classifier that can be used for diagnosing cachexia syndrome and identifying its involved biomarkers. To achieve this goal, a data-driven analysis is carried out using a public dataset consisting of 1H-NMR metabolite profile. This dataset suffers from the problem of imbalanced classes...

The tasks of pre and post-processing in Data Mining applied to a real world problem

Pre and post-processing are crucial tasks in Knowledge Discovery in Databases (KDD). In this contribution we present an application to a data set from a real water supply network (WSN) in the town of Calarcá (Colombia), located in the so-called "Eje Cafetero" coffee region. We use traditional and well-known techniques of pre and post-processing with the aim of showing its importance in Data Min...

Learning from Data Some Real World Considerations