Data mining in large sets of complex data

Author(s)

Robson Leonardo Ferreira Cordeiro

• Document type: Doctoral Thesis
• Press: São Carlos.
• Institution: Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB)
• Defense date: 2011-08-29

Examining board members

Caetano Traina Junior; Christos Faloutsos; João Eduardo Ferreira; Carlos Alberto Heuser; Mirella Moura Moro

• Advisor: Caetano Traina Junior; Christos Faloutsos

Abstract

Due to the increasing amount and complexity of the data stored in the enterprises\' databases, the task of knowledge discovery is nowadays vital to support strategic decisions. However, the mining techniques used in the process usually have high computational costs that come from the need to explore several alternative solutions, in different combinations, to obtain the desired knowledge. The most common mining tasks include data classification, labeling and clustering, outlier detection and missing data prediction. Traditionally, the data are represented by numerical or categorical attributes in a table that describes one element in each tuple. Although the same tasks applied to traditional data are also necessary for more complex data, such as images, graphs, audio and long texts, the complexity and the computational costs associated to handling large amounts of these complex data increase considerably, making most of the existing techniques impractical. Therefore, especial data mining techniques for this kind of data need to be developed. This Ph.D. work focuses on the development of new data mining techniques for large sets of complex data, especially for the task of clustering, tightly associated to other data mining tasks that are performed together. Specifically, this Doctoral dissertation presents three novel, fast and scalable data mining algorithms well-suited to analyze large sets of complex data: the method Halite for correlation clustering; the method BoW for clustering Terabyte-scale datasets; and the method QMAS for labeling and summarization. Our algorithms were evaluated on real, very large datasets with up to billions of complex elements, and they always presented highly accurate results, being at least one order of magnitude faster than the fastest related works in almost all cases. The real data used come from the following applications: automatic breast cancer diagnosis, satellite imagery analysis, and graph mining on a large web graph crawled by Yahoo! and also on the graph with all users and their connections from the Twitter social network. Such results indicate that our algorithms allow the development of real time applications that, potentially, could not be developed without this Ph.D. work, like a software to aid on the fly the diagnosis process in a worldwide Healthcare Information System, or a system to look for deforestation within the Amazon Rainforest in real time (AU)

• FAPESP's process: 07/01639-4 - Data Mining in Large Medical Image Databases
• Grantee: Robson Leonardo Ferreira Cordeiro
• Support Opportunities: Scholarships in Brazil - Doctorate