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SOM-based Clustering of Multilingual Documents Using an Ontology

Related publications (33)

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A simple model to study subspace clustering is the high-dimensional k -Gaussian mixture model where the cluster means are sparse vectors. Here we provide an exact asymptotic characterization of the statistically optimal reconstruction error in this model i ...

2022

New Multi-Keyword Ciphertext Search Method for Sensor Network Cloud Platforms

Jiyong Zhang, Yue Wang, Hongyu Yang

This paper proposed a multi-keyword ciphertext search, based on an improved-quality hierarchical clustering (MCS-IQHC) method. MCS-IQHC is a novel technique, which is tailored to work with encrypted data. It has improved search accuracy and can self-adapt ...

MDPI2018

Robust and Efficient Data Clustering with Signal Processing on Graphs

Lionel Jérémie Martin

Data is pervasive in today's world and has actually been for quite some time. With the increasing volume of data to process, there is a need for faster and at least as accurate techniques than what we already have. In particular, the last decade recorded t ...

EPFL2018

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Collaborative synchronous writing tools like Google Docs and Etherpad let multiple users edit the same document and see each others edits in near real-time to simplify collaboration and avoid merge-conflicts. These tools are used extensively across many do ...

2018

Robust Discriminative Clustering with Sparse Regularizers

Nicolas Henri Bernard Flammarion

Clustering high-dimensional data often requires some form of dimensionality reduction, where clustered variables are separated from "noise-looking" variables. We cast this problem as finding a low-dimensional projection of the data which is well-clustered. ...

2017

Dynamic clustering and propagation of congestion in heterogeneously congested urban traffic networks

Nikolaos Geroliminis, Mohammadreza Saeedmanesh

The problem of clustering in urban traffic networks has been mainly studied in static framework by considering traffic conditions at a given time. Nevertheless, it is important to underline that traffic is a strongly time-variant process and it needs to be ...

2017

Efficient Document Filtering Using Vector Space Topic Expansion and Pattern-Mining: The Case of Event Detection in Microposts

Karl Aberer, Philippe Cudré-Mauroux, Julia Proskurnia, Carlos Castillo

Automatically extracting information from social media is challenging given that social content is often noisy, ambiguous, and inconsistent. However, as many stories break on social channels first before being picked up by mainstream media, developing meth ...

2017

Temporally Coherent Clustering of Student Data

The extraction of student behavior is an important task in educational data mining. A common approach to detect similar behavior patterns is to cluster sequential data. Standard approaches identify clusters at each time step separately and typically show l ...

EDM2016

Typical Industrial Cluster - Chemistry/refinery/petrochemistry

Laurent Gabriel Félix Alexandre Stéphane Bungener

A refinery and petrochemical site are described in the document. Data for the steam consumption and production of the cluster's process units are also described. The cluster can be broken down into two individual sites made up of process units and process ...

2016

Decentralized clustering over adaptive networks

Ali H. Sayed, Ali Zoubir

Cooperation among agents across the network leads to better estimation accuracy. However, in many network applications the agents infer and track different models of interest in an environment where agents do not know beforehand which models are being obse ...

IEEE2015