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Propagation in bipartite graphs for Topic Extraction in Data Streams

Grant number: 11/23689-9
Support Opportunities:Scholarships in Brazil - Doctorate
Effective date (Start): June 01, 2012
Effective date (End): May 10, 2016
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Alneu de Andrade Lopes
Grantee:Thiago de Paulo Faleiros
Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Associated scholarship(s):13/15353-6 - Evaluating syntactic topic models, BE.EP.DR


A lot of of research is devoted to dynamic data mining, which generally contains large amounts of solid, multi-dimensional and high speed datas. Such data are common in everyday applications, such as telephone calls, retail sales, performance, data center, manufacturing operations, among others. A instance particular problem is exploration and mining of textual data stream collections. This relevant area, which analysts are interested in behavior perception and data trends, still imposes many challenges to researchers. In this context, this project aims to investigate innovative text mining techniques for topics extraction from data stream. Large collections of text data are considered, typically news contents, Web-logs etc. The publications of these data happen in real time, which results in constant topics changes distribution. The scope of this proposal includes all the text mining steps related to topics extraction from stream and, considering concepts drifts. To do this, will be explored and developed text mining technologies for extraction and identification of dynamic topics, information extraction over time and texts categorization for temporal coverage. The results of this PhD project will be validated through benchmark and techniques available for text mining and Data Streaming.

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Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
BERTON, LILIAN; FALEIROS, THIAGO DE PAULO; VALEJO, ALAN; VALVERDE-REBAZA, JORGE; LOPES, ALNEU DE ANDRADE. RGCLI: Robust Graph that Considers Labeled Instances for Semi Supervised Learning. Neurocomputing, v. 226, p. 238-248, . (11/23689-9, 11/21880-3, 15/14228-9, 13/12191-5)
ROSSI, RAFAEL GERALDELI; LOPES, ALNEU DE ANDRADE; FALEIROS, THIAGO DE PAULO; REZENDE, SOLANGE OLIVEIRA. Inductive Model Generation for Text Classification Using a Bipartite Heterogeneous Network. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, v. 29, n. 3, p. 361-375, . (11/12823-6, 11/23689-9, 11/19850-9)
FALEIROS, THIAGO DE PAULO; VALEJO, ALAN; LOPES, ALNEU DE ANDRADE. Unsupervised learning of textual pattern based on Propagation in Bipartite Graph. Intelligent Data Analysis, v. 24, n. 3, p. 543-565, . (11/23689-9, 15/14228-9)
Academic Publications
(References retrieved automatically from State of São Paulo Research Institutions)
FALEIROS, Thiago de Paulo. Propagation in bipartite graphs for topic extraction in stream of textual data. 2016. Doctoral Thesis - Universidade de São Paulo (USP). Instituto de Ciências Matemáticas e de Computação (ICMC/SB) São Carlos.

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