Fuzzy set methods for object recognition in space applications

Progress on the following tasks is reported: (1) fuzzy set-based decision making methodologies; (2) feature calculation; (3) clustering for curve and surface fitting; and (4) acquisition of images. The general structure for networks based on fuzzy set connectives which are being used for information fusion and decision making in space applications is described. The structure and training techniques for such networks consisting of generalized means and gamma-operators are described. The use of other hybrid operators in multicriteria decision making is currently being examined. Numerous classical features on image regions such as gray level statistics, edge and curve primitives, texture measures from cooccurrance matrix, and size and shape parameters were implemented. Several fractal geometric features which may have a considerable impact on characterizing cluttered background, such as clouds, dense star patterns, or some planetary surfaces, were used. A new approach to a fuzzy C-shell algorithm is addressed. NASA personnel are in the process of acquiring suitable simulation data and hopefully videotaped actual shuttle imagery. Photographs have been digitized to use in the algorithms. Also, a model of the shuttle was assembled and a mechanism to orient this model in 3-D to digitize for experiments on pose estimation is being constructed

Survey of data mining approaches to user modeling for adaptive hypermedia

The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. Some of the difficulties that user modeling faces are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for automatic generation of user models that simulate human decision making. This paper surveys different data mining techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques may be used more efficiently according to the task implemented by the applicatio

Techniques for clustering gene expression data

Many clustering techniques have been proposed for the analysis of gene expression data obtained from microarray experiments. However, choice of suitable method(s) for a given experimental dataset is not straightforward. Common approaches do not translate well and fail to take account of the data profile. This review paper surveys state of the art applications which recognises these limitations and implements procedures to overcome them. It provides a framework for the evaluation of clustering in gene expression analyses. The nature of microarray data is discussed briefly. Selected examples are presented for the clustering methods considered

Determining the number of clusters and distinguishing overlapping clusters in data analysis

Le processus de Clustering permet de construire une collection d’objets (clusters) similaires au sein d’un même groupe, et dissimilaires quand ils appartiennent à des groupes différents. Dans cette thèse, on s’intéresse a deux problèmes majeurs d’analyse de données: 1) la détermination automatique du nombre de clusters dans un ensemble de données dont on a aucune information sur les structures qui le composent; 2) le phénomène de recouvrement entre les clusters. La plupart des algorithmes de clustering souffrent du problème de la détermination du nombre de clusters qui est souvent laisse à l’utilisateur. L’approche classique pour déterminer le nombre de clusters est basée sur un processus itératif qui minimise une fonction objectif appelé indice de validité. Notre but est de: 1) développer un nouvel indice de validité pour mesurer la qualité d’une partition, qui est le résultat d’un algorithme de clustering; 2) proposer un nouvel algorithme de clustering flou pour déterminer automatiquement le nombre de clusters. Une application de notre nouvel algorithme est présentée. Elle consiste à la sélection des caractéristiques dans une base de données. Le phénomène de recouvrement entre les clusters est un des problèmes difficile dans la reconnaissance de formes statistiques. La plupart des algorithmes de clustering ont des difficultés à distinguer les clusters qui se chevauchent. Dans cette thèse, on a développé une théorie qui caractérise le phénomène de recouvrement entre les clusters dans un modèle de mélange Gaussien d’une manière formelle. À partir de cette théorie, on a développé un nouvel algorithme qui calcule le degré de recouvrement entre les clusters dans le cas multidimensionnel. Dans ce cadre précis, on a étudié les facteurs qui affectent la valeur théorique du degré de recouvrement. On a démontré comment cette théorie peut être utilisée pour la génération des données de test valides et concrètes pour une évaluation objective des indices de validité pax rapport à leurs capacités à distinguer les clusters qui se chevauchent. Finalement, notre théorie est utilisable dans une application de segmentation des images couleur en utilisant un algorithme de clustering hiérarchique

Recent Developments in Document Clustering

This report aims to give a brief overview of the current state of document clustering research and present recent developments in a well-organized manner. Clustering algorithms are considered with two hypothetical scenarios in mind: online query clustering with tight efficiency constraints, and offline clustering with an emphasis on accuracy. A comparative analysis of the algorithms is performed along with a table summarizing important properties, and open problems as well as directions for future research are discussed

Observer-biased bearing condition monitoring: from fault detection to multi-fault classification

Bearings are simultaneously a fundamental component and one of the principal causes of failure in rotary machinery. The work focuses on the employment of fuzzy clustering for bearing condition monitoring, i.e., fault detection and classification. The output of a clustering algorithm is a data partition (a set of clusters) which is merely a hypothesis on the structure of the data. This hypothesis requires validation by domain experts. In general, clustering algorithms allow a limited usage of domain knowledge on the cluster formation process. In this study, a novel method allowing for interactive clustering in bearing fault diagnosis is proposed. The method resorts to shrinkage to generalize an otherwise unbiased clustering algorithm into a biased one. In this way, the method provides a natural and intuitive way to control the cluster formation process, allowing for the employment of domain knowledge to guiding it. The domain expert can select a desirable level of granularity ranging from fault detection to classification of a variable number of faults and can select a specific region of the feature space for detailed analysis. Moreover, experimental results under realistic conditions show that the adopted algorithm outperforms the corresponding unbiased algorithm (fuzzy c-means) which is being widely used in this type of problems. (C) 2016 Elsevier Ltd. All rights reserved.Grant number: 145602

Multi-objective evolutionary fuzzy clustering for high-dimensional problems

This paper deals with the application of unsupervised fuzzy clustering to high dimensional data. Two problems are addressed: groups (clusters) number discovery and feature selection without performance losses. In particular we analyze the potential of a genetic fuzzy system, that is the integration of a multi-objective evolutionary algorithm with a fuzzy clustering algorithm. The main characteristic of the integrated approach is the ability to handle the two problems at the same time, suggesting a Pareto set of trade-off solutions which could have a better chance of matching the real needs. We exhibit the high quality clustering and features selection results by applying our approach to a real-world data set

A review of clustering techniques and developments

© 2017 Elsevier B.V. This paper presents a comprehensive study on clustering: exiting methods and developments made at various times. Clustering is defined as an unsupervised learning where the objects are grouped on the basis of some similarity inherent among them. There are different methods for clustering the objects such as hierarchical, partitional, grid, density based and model based. The approaches used in these methods are discussed with their respective states of art and applicability. The measures of similarity as well as the evaluation criteria, which are the central components of clustering, are also presented in the paper. The applications of clustering in some fields like image segmentation, object and character recognition and data mining are highlighted