55,144 research outputs found
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
Dynamic Clustering of Histogram Data Based on Adaptive Squared Wasserstein Distances
This paper deals with clustering methods based on adaptive distances for
histogram data using a dynamic clustering algorithm. Histogram data describes
individuals in terms of empirical distributions. These kind of data can be
considered as complex descriptions of phenomena observed on complex objects:
images, groups of individuals, spatial or temporal variant data, results of
queries, environmental data, and so on. The Wasserstein distance is used to
compare two histograms. The Wasserstein distance between histograms is
constituted by two components: the first based on the means, and the second, to
internal dispersions (standard deviation, skewness, kurtosis, and so on) of the
histograms. To cluster sets of histogram data, we propose to use Dynamic
Clustering Algorithm, (based on adaptive squared Wasserstein distances) that is
a k-means-like algorithm for clustering a set of individuals into classes
that are apriori fixed.
The main aim of this research is to provide a tool for clustering histograms,
emphasizing the different contributions of the histogram variables, and their
components, to the definition of the clusters. We demonstrate that this can be
achieved using adaptive distances. Two kind of adaptive distances are
considered: the first takes into account the variability of each component of
each descriptor for the whole set of individuals; the second takes into account
the variability of each component of each descriptor in each cluster. We
furnish interpretative tools of the obtained partition based on an extension of
the classical measures (indexes) to the use of adaptive distances in the
clustering criterion function. Applications on synthetic and real-world data
corroborate the proposed procedure
Finding groups in data: Cluster analysis with ants
Wepresent in this paper a modification of Lumer and Faieta’s algorithm for data clustering. This approach
mimics the clustering behavior observed in real ant colonies. This algorithm discovers automatically
clusters in numerical data without prior knowledge of possible number of clusters. In this paper we focus
on ant-based clustering algorithms, a particular kind of a swarm intelligent system, and on the effects on
the final clustering by using during the classification differentmetrics of dissimilarity: Euclidean, Cosine,
and Gower measures. Clustering with swarm-based algorithms is emerging as an alternative to more
conventional clustering methods, such as e.g. k-means, etc. Among the many bio-inspired techniques, ant
clustering algorithms have received special attention, especially because they still require much
investigation to improve performance, stability and other key features that would make such algorithms
mature tools for data mining.
As a case study, this paper focus on the behavior of clustering procedures in those new approaches.
The proposed algorithm and its modifications are evaluated in a number of well-known benchmark
datasets. Empirical results clearly show that ant-based clustering algorithms performs well when
compared to another techniques
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