A new topological clustering algorithm for interval data
Clustering is a very powerful tool for automatic detection of relevant sub-groups in unlabeled
data sets. In this paper we focus on interval data: ie, where the objects are defined as hyper …
data sets. In this paper we focus on interval data: ie, where the objects are defined as hyper …
[PDF][PDF] Learning the number of clusters in self organizing map
The Self-Organizing Map (SOM: Kohonen (1984, 2001)) is a neuro-computational algorithm
to map high-dimensional data to a two-dimensional space through a competitive and …
to map high-dimensional data to a two-dimensional space through a competitive and …
Enriched topological learning for cluster detection and visualization
The exponential growth of data generates terabytes of very large databases. The growing
number of data dimensions and data objects presents tremendous challenges for effective …
number of data dimensions and data objects presents tremendous challenges for effective …
An optimal baseline selection methodology for data-driven damage detection and temperature compensation in acousto-ultrasonics
MA Torres-Arredondo, J Sierra-Pérez… - Smart Materials and …, 2016 - iopscience.iop.org
The process of measuring and analysing the data from a distributed sensor network all over
a structural system in order to quantify its condition is known as structural health monitoring …
a structural system in order to quantify its condition is known as structural health monitoring …
A local density-based simultaneous two-level algorithm for topographic clustering
Determining the optimum number of clusters is an ill posed problem for which there is no
simple way of knowing that number without a priori knowledge. The purpose of this paper is …
simple way of knowing that number without a priori knowledge. The purpose of this paper is …
Smart aeronautical structures: development and experimental validation of a structural health monitoring system for damage detection
J Sierra Perez - 2014 - oa.upm.es
In many engineering fields, the integrity and reliability of the structures are extremely
important aspects. They are controlled by the adequate knowledge of existing damages …
important aspects. They are controlled by the adequate knowledge of existing damages …
Mining RFID behavior data using unsupervised learning
Abstract Radio Frequency IDentification (RFID) is an advanced tracking technology that can
be used to study the spatial organization of individual's spatio-temporal activity. The aim of …
be used to study the spatial organization of individual's spatio-temporal activity. The aim of …
Topographic connectionist unsupervised learning for RFID behavior data mining
Radio Frequency IDentification (RFID) is an advanced tracking technology that can be used
to study the spatial organization of animal societies. The aim of this work is to build a new …
to study the spatial organization of animal societies. The aim of this work is to build a new …
[PDF][PDF] Two level clustering untuk analisis kuesioner akademik di STTA Yogyakarta
H Agustian, S Hartati… - Angkasa: Jurnal Ilmiah …, 2018 - scholar.archive.org
Untuk menjaga kualitas dosen, institusi melakukan evaluasi kineija dosen yang dapat
berupa kuesioner akademik dimana kadang hasilnya bersifat subyektif. Untuk itu perlu …
berupa kuesioner akademik dimana kadang hasilnya bersifat subyektif. Untuk itu perlu …
Unsupervised Learning of Data Representations and Cluster Structures: Applications to Large-scale Health Monitoring of Turbofan Aircraft Engines
F Forest - 2021 - theses.hal.science
This thesis is interested in unsupervised statistical learning methods and their applications
to health monitoring of aircraft engines at an industrial scale. Our first objective is to make …
to health monitoring of aircraft engines at an industrial scale. Our first objective is to make …