Banca de QUALIFICAÇÃO: YURI THOMAS PINHEIRO NUNES

Uma banca de QUALIFICAÇÃO de DOUTORADO foi cadastrada pelo programa.
STUDENT : YURI THOMAS PINHEIRO NUNES
DATE: 16/12/2020
TIME: 09:00
LOCAL: Videoconferência: meet.google.com/ffq-prgu-hix
TITLE:

Dynamic clustering based on evolving system approach

KEY WORDS:

Data Streams, Evolving Algorithms, Hidden Markov Models, Evolving Systems, TEDA

PAGES: 50
BIG AREA: Ciências Exatas e da Terra
AREA: Ciência da Computação
SUBÁREA: Matemática da Computação
SPECIALTY: Modelos Analíticos e de Simulação
SUMMARY:

The main objective of this work is to devise an algorithm for time series or ordered data sequences processing. These data sources can be considered as continuous and theoretically infinite data streams. Assuming times series and data sequences as data streams enables using evolving algorithms for the single data pass and cumulative knowledge extraction. This new algorithm is strongly inspired by hidden Markov models (HMM's) and AutoCloud (a evolving algorithm for data clustering), the later is based on TEDA (Typicality and Eccentricity Data Analysis) which will also be used. The AutoCloud will serve as a base to model data patterns similar to the states of HMMs and TEDA will be used to estimate the state transitions thus obtaining a model similar to a traditional HMM. Initially, there will be proposed modifications to AutoCloud to improve the algorithm performance regarding concept drift and concept evolution, also in the cluster merge operation and inclusion for a cluster split operation Furthermore, there will be defined an strategy to calculate the most typical transitions between clusters. It is expected that the performance of AutoCloud does not drop in known benchmarks but also to become more robust when dealing with new datasets.

BANKING MEMBERS:
Presidente - 1153006 - LUIZ AFFONSO HENDERSON GUEDES DE OLIVEIRA
Interno - 2579664 - ALLAN DE MEDEIROS MARTINS
Externo à Instituição - CLAUBER GOMES BEZERRA - IFRN