10 citations · 18 across the 5 of their papers we have counts for
6 papers
MaxMin Linear Initialization for Fuzzy C-Means
Aybükë Oztürk, Stéphane Lallich, Jérôme Darmont +1
Clustering is an extensive research area in data science. The aim of clustering is to discover groups and to identify interesting patterns in datasets. Crisp (hard) clustering cons…
A Visual Quality Index for Fuzzy C-Means
Aybükë Oztürk, Stéphane Lallich, Jérôme Darmont
Cluster analysis is widely used in the areas of machine learning and data mining. Fuzzy clustering is a particular method that considers that a data point can belong to more than o…
Warehousing Complex Archaeological Objects
Aybükë Oztürk, Louis Eyango, Sylvie Yona Waksman +2
Data organization is a difficult and essential component in cultural heritage applications. Over the years, a great amount of archaeological ceramic data have been created and proc…
How to Use Temporal-Driven Constrained Clustering to Detect Typical Evolutions
Marian-Andrei Rizoiu, Julien Velcin, Stéphane Lallich
In this paper, we propose a new time-aware dissimilarity measure that takes into account the temporal dimension. Observations that are close in the description space, but distant i…
Semantic-enriched Visual Vocabulary Construction in a Weakly Supervised Context
Marian-Andrei Rizoiu, Julien Velcin, Stéphane Lallich
One of the prevalent learning tasks involving images is content-based image classification. This is a difficult task especially because the low-level features used to digitally des…
ClusPath: A Temporal-driven Clustering to Infer Typical Evolution Paths
Marian-Andrei Rizoiu, Julien Velcin, Stéphane Bonnevay +1
We propose ClusPath, a novel algorithm for detecting general evolution tendencies in a population of entities. We show how abstract notions, such as the Swedish socio-economical mo…