Analyse de grappe des données de catégories et de séquences étude et application à la prédiction de la faillite personnelle

Cluster analysis is one of the most important and useful data mining techniques, and there are many applications of cluster analysis in pattern extraction, information retrieval, summarization, compression and other areas. The focus of this thesis is on clustering categorical and sequence data. Clustering categorical and sequence data is much more challenging than clustering numeric data because there is no inherently meaningful measure of similarity between the categorical objects and sequences. In this thesis, we design novel efficient and effective clustering algorithms for clustering categorical data and sequence respectively, and we perform extensive experiments to demonstrate the superior performance of our proposed algorithm. We also explore the extent to which the use of the proposed clustering algorithms can help to solve the personal bankruptcy prediction problem. Clustering categorical data poses two challenges: defining an inherently meaningful similarity measure, and effectively dealing with clusters which are often embedded in different subspaces. In this thesis, we view the task of clustering categorical data from an optimization perspective and propose a novel objective function. Based on the new formulation, we design a divisive hierarchical clustering algorithm for categorical data, named DHCC. In the bisection procedure of DHCC, the initialization of the splitting is based on multiple correspondence analysis (MCA). We devise a strategy for dealing with the key issue in the divisive approach, namely, when to terminate the splitting process. The proposed algorithm is parameter-free, independent of the order in which the data is processed, scalable to large data sets and capable of seamlessly discovering clusters embedded in subspaces. The prior knowledge about the data can be incorporated into the clustering process, which is known as semi-supervised clustering, to produce considerable improvement in learning accuracy. In this thesis, we view semi-supervised clustering of categorical data as an optimization problem with extra instance-level constraints, and propose a systematic and fully automated approach to guide the optimization process to a better solution in terms of satisfying the constraints, which would also be beneficial to the unconstrained objects. The proposed semi-supervised divisive hierarchical clustering algorithm for categorical data, named SDHCC, is parameter-free, fully automatic and effective in taking advantage of instance-level constraint background knowledge to improve the quality of the resultant dendrogram. Many existing sequence clustering algorithms rely on a pair-wise measure of similarity between sequences. Usually, such a measure is effective if there are significantly informative patterns in the sequences. However, it is difficult to define a meaningful pair-wise similarity measure if sequences are short and contain noise. In this thesis, we circumvent the obstacle of defining the pairwise similarity by defining the similarity between an individual sequence and a set of sequences. Based on the new similarity measure, which is based on the conditional probability distribution (CPD) model, we design a novel model-based K -means clustering algorithm for sequence clustering, which works in a similar way to the traditional K -means on vectorial data. Finally, we develop a personal bankruptcy prediction system whose predictors are mainly the bankruptcy features discovered by the clustering techniques proposed in this thesis. The mined bankruptcy features are represented in low-dimensional vector space. From the new feature space, which can be extended with some existing prediction-capable features (e.g., credit score), a support vector machine (SVM) classifier is built to combine these mined and already existing features. Our system is readily comprehensible and demonstrates promising prediction performance

A General Spatio-Temporal Clustering-Based Non-local Formulation for Multiscale Modeling of Compartmentalized Reservoirs

Representing the reservoir as a network of discrete compartments with neighbor and non-neighbor connections is a fast, yet accurate method for analyzing oil and gas reservoirs. Automatic and rapid detection of coarse-scale compartments with distinct static and dynamic properties is an integral part of such high-level reservoir analysis. In this work, we present a hybrid framework specific to reservoir analysis for an automatic detection of clusters in space using spatial and temporal field data, coupled with a physics-based multiscale modeling approach. In this work a novel hybrid approach is presented in which we couple a physics-based non-local modeling framework with data-driven clustering techniques to provide a fast and accurate multiscale modeling of compartmentalized reservoirs. This research also adds to the literature by presenting a comprehensive work on spatio-temporal clustering for reservoir studies applications that well considers the clustering complexities, the intrinsic sparse and noisy nature of the data, and the interpretability of the outcome. Keywords: Artificial Intelligence; Machine Learning; Spatio-Temporal Clustering; Physics-Based Data-Driven Formulation; Multiscale Modelin

A taxonomy framework for unsupervised outlier detection techniques for multi-type data sets

The term "outlier" can generally be defined as an observation that is significantly different from the other values in a data set. The outliers may be instances of error or indicate events. The task of outlier detection aims at identifying such outliers in order to improve the analysis of data and further discover interesting and useful knowledge about unusual events within numerous applications domains. In this paper, we report on contemporary unsupervised outlier detection techniques for multiple types of data sets and provide a comprehensive taxonomy framework and two decision trees to select the most suitable technique based on data set. Furthermore, we highlight the advantages, disadvantages and performance issues of each class of outlier detection techniques under this taxonomy framework

Element-centric clustering comparison unifies overlaps and hierarchy

Clustering is one of the most universal approaches for understanding complex data. A pivotal aspect of clustering analysis is quantitatively comparing clusterings; clustering comparison is the basis for many tasks such as clustering evaluation, consensus clustering, and tracking the temporal evolution of clusters. In particular, the extrinsic evaluation of clustering methods requires comparing the uncovered clusterings to planted clusterings or known metadata. Yet, as we demonstrate, existing clustering comparison measures have critical biases which undermine their usefulness, and no measure accommodates both overlapping and hierarchical clusterings. Here we unify the comparison of disjoint, overlapping, and hierarchically structured clusterings by proposing a new element-centric framework: elements are compared based on the relationships induced by the cluster structure, as opposed to the traditional cluster-centric philosophy. We demonstrate that, in contrast to standard clustering similarity measures, our framework does not suffer from critical biases and naturally provides unique insights into how the clusterings differ. We illustrate the strengths of our framework by revealing new insights into the organization of clusters in two applications: the improved classification of schizophrenia based on the overlapping and hierarchical community structure of fMRI brain networks, and the disentanglement of various social homophily factors in Facebook social networks. The universality of clustering suggests far-reaching impact of our framework throughout all areas of science

A survey of outlier detection methodologies

Outlier detection has been used for centuries to detect and, where appropriate, remove anomalous observations from data. Outliers arise due to mechanical faults, changes in system behaviour, fraudulent behaviour, human error, instrument error or simply through natural deviations in populations. Their detection can identify system faults and fraud before they escalate with potentially catastrophic consequences. It can identify errors and remove their contaminating effect on the data set and as such to purify the data for processing. The original outlier detection methods were arbitrary but now, principled and systematic techniques are used, drawn from the full gamut of Computer Science and Statistics. In this paper, we introduce a survey of contemporary techniques for outlier detection. We identify their respective motivations and distinguish their advantages and disadvantages in a comparative review