Search results for "DATA MINING"

Estimating finite mixtures of semi-Markov chains: an application to the segmentation of temporal sensory data

2019

Summary In food science, it is of great interest to obtain information about the temporal perception of aliments to create new products, to modify existing products or more generally to understand the mechanisms of perception. Temporal dominance of sensations is a technique to measure temporal perception which consists in choosing sequentially attributes describing a food product over tasting. This work introduces new statistical models based on finite mixtures of semi-Markov chains to describe data collected with the temporal dominance of sensations protocol, allowing different temporal perceptions for a same product within a population. The identifiability of the parameters of such mixtur…

Fuzzy Systems Based on Multispecies PSO Method in Spatial Analysis

2012

We present a method by using the hierarchical cluster-based Multispecies particle swarm optimization to generate a fuzzy system of Takagi-Sugeno-Kang type encapsulated in a geographical information system considered as environmental decision support for spatial analysis. We consider a spatial area partitioned in subzones: the data measured in each subzone are used to extract a fuzzy rule set of above mentioned type. We adopt a similarity index (greater than a specific threshold) for comparing fuzzy systems generated for adjacent subzones.

On data mining applications in mobile networking and network security

2014

Classifying DME vs Normal SD-OCT volumes: A review

2016

International audience; This article reviews the current state of automatic classification methodologies to identify Diabetic Macular Edema (DME) versus normal subjects based on Spectral Domain OCT (SD-OCT) data. Addressing this classification problem has valuable interest since early detection and treatment of DME play a major role to prevent eye adverse effects such as blindness. The main contribution of this article is to cover the lack of a public dataset and benchmark suited for classifying DME and normal SD-OCT volumes, providing our own implementation of the most relevant methodologies in the literature. Subsequently, 6 different methods were implemented and evaluated using this comm…

Investigating Long-Range Dependence in E-Commerce Web Traffic

2016

This paper addresses the problem of investigating long-range dependence (LRD) and self-similarity in Web traffic. Popular techniques for estimating the intensity of LRD via the Hurst parameter are presented. Using a set of traces of a popular e-commerce site, the presence and the nature of LRD in Web traffic is examined. Our results confirm the self-similar nature of traffic at a Web server input, however the resulting estimates of the Hurst parameter vary depending on the trace and the technique used.

Towards a Great Design of Conceptual Modelling

2020

Humankind faces a most crucial mission; we must endeavour, on a global scale, to restore and improve our natural and social environments. This is a big challenge for global information systems development and for their modelling. In this paper, we discuss on different aspects of conceptual modelling in global environmental context. The paper is the summary of the panel session “The Future of Conceptual Modelling” in the 29th International Conference on Information Modelling and Knowledge Bases. peerReviewed

Unstable feature relevance in classification tasks

2011

Knowledge discovery using diffusion maps

2013

3D MODELING OF TWO LOUTERIA FRAGMENTS BY IMAGE-BASED APPROACH

2017

Abstract. The paper presents a digital approach to the reconstruction and analysis of two small-sized fragments of louteria, a kind of large terracotta vase, found during an archaeological survey in the south of Sicily (Italy), in the area of Cignana near the Greek colony of Akragas (nowadays Agrigento). The fragments of louteria have been studied by an image-based approach in order to achieve high accurate and very detailed 3D models. The 3D models have been used to carry out interpretive and geometric analysis from an archaeological point of view. Using different digital tools, it was possible to highlight some fine details of the louteria decorations and to better understand the characte…

CLUSTERING INCOMPLETE SPECTRAL DATA WITH ROBUST METHODS

2018

Abstract. Missing value imputation is a common approach for preprocessing incomplete data sets. In case of data clustering, imputation methods may cause unexpected bias because they may change the underlying structure of the data. In order to avoid prior imputation of missing values the computational operations must be projected on the available data values. In this paper, we apply a robust nan-K-spatmed algorithm to the clustering problem on hyperspectral image data. Robust statistics, such as multivariate medians, are more insensitive to outliers than classical statistics relying on the Gaussian assumptions. They are, however, computationally more intractable due to the lack of closed-for…