Conceptual Clustering of Structured Objects

Author(s):  
R. E. Stepp
1980 ◽  
Vol 3 (2) ◽  
pp. 171-180
Author(s):  
J.A. Bergstra ◽  
H.J.M. Goeman ◽  
A. Ollongren ◽  
G.A. Terpstra ◽  
Th.P. van der Weide
Keyword(s):  

A set of axioms for structured objects of data is presented. In the structured objects components and levels are distinguished. Change of level is the result of a special application operator, components are accessible by successive selections. The set of access paths is also axiomatized. The set of axioms is uniform in the sense that features of various known classes of datastructures are combined.


2004 ◽  
Vol 56 (1-3) ◽  
pp. 115-151 ◽  
Author(s):  
Nina Mishra ◽  
Dana Ron ◽  
Ram Swaminathan

Author(s):  
S. Ferilli ◽  
T. M. A. Basile ◽  
N. Di Mauro ◽  
M. Biba ◽  
F. Esposito

2020 ◽  
Vol 67 ◽  
pp. 509-547
Author(s):  
Maxime Chabert ◽  
Christine Solnon

We introduce the exactCover global constraint dedicated to the exact cover problem, the goal of which is to select subsets such that each element of a given set belongs to exactly one selected subset. This NP-complete problem occurs in many applications, and we more particularly focus on a conceptual clustering application. We introduce three propagation algorithms for exactCover, called Basic, DL, and DL+: Basic ensures the same level of consistency as arc consistency on a classical decomposition of exactCover into binary constraints, without using any specific data structure; DL ensures the same level of consistency as Basic but uses Dancing Links to efficiently maintain the relation between elements and subsets; and DL+ is a stronger propagator which exploits an extra property to filter more values than DL. We also consider the case where the number of selected subsets is constrained to be equal to a given integer variable k, and we show that this may be achieved either by combining exactCover with existing constraints, or by designing a specific propagator that integrates algorithms designed for the NValues constraint. These different propagators are experimentally evaluated on conceptual clustering problems, and they are compared with state-of-the-art declarative approaches. In particular, we show that our global constraint is competitive with recent ILP and CP models for mono-criterion problems, and it has better scale-up properties for multi-criteria problems.


Author(s):  
Donato Malerba ◽  
Annalisa Appice ◽  
Antonio Varlaro ◽  
Antonietta Lanza

Author(s):  
Floriana Esposito ◽  
Nicola Fanizzi ◽  
Claudia d’Amato

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