Thesis etd-01282019-164923 |
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Thesis type
Tesi di laurea magistrale
URN
etd-01282019-164923
Thesis title
A novel fuzzy density-based clustering algorithm for streaming data
Department
INGEGNERIA DELL'INFORMAZIONE
Course of study
COMPUTER ENGINEERING
Supervisors
.
relatore Prof. Marcelloni, Francesco
relatore Ing. Bechini, Alessio
correlatore Dott. Renda, Alessandro
relatore Ing. Bechini, Alessio
correlatore Dott. Renda, Alessandro
Keywords
- concept drift
- fuzzy DbScan
- streaming
- temporal decay
Graduation session start date
22/02/2019
Availability
Withheld
Release date
22/02/2089
Abstract (Inglese)
Abstract (Italiano)
The aim of this thesis is the deepening of the principal clustering techniques and frameworks to process data in streaming and the implementation of a novel fuzzy density based algorithm .
Thesis evolves in three parts.The first part is dedicated to the study and description of principal Apache open source frameworks used for streaming. The second part is devoted to development and comparison between a crisp and a fuzzy version of a streaming algorithm in absence of temporal fading.
In the last part is discussed an online-offline version of the algorithm with temporal fading . In this part online phase collects some aggregations in form of vector of neighbours and vector of borders and offline phase computes clusters at some time intervals.
Thesis evolves in three parts.The first part is dedicated to the study and description of principal Apache open source frameworks used for streaming. The second part is devoted to development and comparison between a crisp and a fuzzy version of a streaming algorithm in absence of temporal fading.
In the last part is discussed an online-offline version of the algorithm with temporal fading . In this part online phase collects some aggregations in form of vector of neighbours and vector of borders and offline phase computes clusters at some time intervals.
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