|
|
|
|
|
Sami Diaf and Ulrich Fritsche
This paper proposes a new methodology to study sequential corpora by implementing a two-stage algorithm that learns time-based topics with respect to a scale of document positions and introduces the concept of Topic Scaling, which ranks learned topics wi...
ver más
|
|
|
|
|
|
|
|
|
Consolata Gakii, Paul O. Mireji and Richard Rimiru
Analysis of high-dimensional data, with more features (p" role="presentation">??p
p
) than observations (N" role="presentation">??N
N
) (p>N" role="presentation">??>??p>N
p
>
N
), places significant demand in cost and memory computational usage at...
ver más
|
|
|
|
|
|
|
|
|
Diego Santoro, Andrea Tonon and Fabio Vandin
Sequential pattern mining is a fundamental data mining task with application in several domains. We study two variants of this task?the first is the extraction of frequent sequential patterns, whose frequency in a dataset of sequential transactions is hi...
ver más
|
|
|
|
|
|
|
|
|
Xinhua Wang, Xuemeng Yu, Lei Guo, Fangai Liu and Liancheng Xu
As students? behaviors are important factors that can reflect their learning styles and living habits on campus, extracting useful features of them plays a helpful role in understanding the students? learning process, which is an important step towards p...
ver más
|
|
|
|
|
|
|
|
|
Andrea Brunello, Enrico Marzano, Angelo Montanari and Guido Sciavicco
Temporal information plays a very important role in many analysis tasks, and can be encoded in at least two different ways. It can be modeled by discrete sequences of events as, for example, in the business intelligence domain, with the aim of tracking t...
ver más
|
|
|
|