MaxMin Linear Initialization for Fuzzy C-Means

no code implementations • 1 Aug 2018 • Aybükë Oztürk, Stéphane Lallich, Jérôme Darmont, Sylvie Yona Waksman

The aim of clustering is to discover groups and to identify interesting patterns in datasets.

Clustering Text Categorization

A Visual Quality Index for Fuzzy C-Means

no code implementations • 5 Jun 2018 • Aybükë Oztürk, Stéphane Lallich, Jérôme Darmont

Fuzzy clustering is a particular method that considers that a data point can belong to more than one cluster.

Clustering Text Categorization

How to Use Temporal-Driven Constrained Clustering to Detect Typical Evolutions

no code implementations • 11 Jan 2016 • 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.

Constrained Clustering

Unsupervised Feature Construction for Improving Data Representation and Semantics

no code implementations • 17 Dec 2015 • Marian-Andrei Rizoiu, Julien Velcin, Stéphane Lallich

We seek to construct, in an unsupervised way, new features that are more appropriate for describing a given dataset and, at the same time, comprehensible for a human user.

Two-sample testing

Semantic-enriched Visual Vocabulary Construction in a Weakly Supervised Context

no code implementations • 14 Dec 2015 • Marian-Andrei Rizoiu, Julien Velcin, Stéphane Lallich

We apply our proposition to the task of content-based image classification and we show that semantically enriching the image representation yields higher classification performances than the baseline representation.

Classification General Classification +1