Manifold learning can discover the structure of high dimensional data and provides understanding of multidimensional patterns by preserving the local geometric characteristics. Ho...
Frequent structure mining (FSM) aims to discover and extract patterns frequently occurring in structural data, such as trees and graphs. FSM finds many applications in bioinformat...
Mining frequent patterns with an FP-tree avoids costly candidate generation and repeatedly occurrence frequency checking against the support threshold. It therefore achieves bette...
Large 0-1 datasets arise in various applications, such as market basket analysis and information retrieval. We concentrate on the study of topic models, aiming at results which in...
This paper presents a partitional dynamic clustering method for interval data based on adaptive Hausdorff distances. Dynamic clustering algorithms are iterative two-step relocatio...
Francisco de A. T. de Carvalho, Renata M. C. R. de...