Methods of Hierarchical Clustering

We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self-organizing maps, and mixture models. We review grid-based clustering, focusing on hierarchical density-based approaches. Finally we describe a recently developed very efficient (linear time) hierarchical clustering algorithm, which can also be viewed as a hierarchical grid-based algorithm.Comment: 21 pages, 2 figures, 1 table, 69 reference

Building shared knowledge for EOR technologies: Screening guideline constructions, dashboards, and advanced data analysis

Successful implementation of enhanced oil recovery (EOR) technology requires comprehensive knowledge and experiences based on existing EOR projects. EOR screening guidelines and EOR reservoir analog are served as such knowledge which are considered as the first step for a reservoir engineer to determine the next step techniques to improve the ultimate oil recovery from their assets. The objective of this research work is to provide better assistance for EOR selection by using fundamental statistics methods and machine learning techniques. In this dissertation, a total of 977 worldwide EOR projects with the most uniformed, high-quality, and comprehensive information were collected from scattered publications and sources, which lays the foundation for further analysis and reasoning. Conventional screening guidelines for 12 EOR technologies were updated with the augment of critical parameters (e.g. MMP, net thickness) compared with previous studies. Hierarchical clustering and principal component analysis are applied for the construction of advanced EOR screening models. Furthermore, a hybrid EOR screening system was established with the combination of conventional and advanced screening technology. Finally, reservoir analog technology was applied to the steam flooding projects to detect the most similar case to assist the decision-making process with limited data information. The results show wider applicability from conventional guidelines; an advanced EOR selection model with discriminative screening results; a hybrid model which combines the advantages of conventional and advanced screening technologies; and an accurate reservoir analog results for steam flooding projects --Abstract, page iv

Recognition by directed attention to recursively partitioned images

A learning/recognition model (and instantiating program) is described which recursively combines the learning paradigms of conceptual clustering (Michalski, 1980) and learning-from-examples to resolve the ambiguities of real-world recognition. The model is based on neuropsychological and psychological evidence that the visual system is analytic, hierarchical, and composed of a parallel/serial dichotomy (many, see conclusions by Crick, 1984). Emulating the experimental evidence, parallel processes in the model decompose the image into components and cluster the constituents in much the same way as the image processing technique known as moment analysis (Alt, 1962). Serial, attentive mechanisms then reassemble the decompositions by investigating spatial relationships between components. The use of attentive mechanisms extends the moment analysis technique to handle alterations in structure and solves the contention problem created by combining the two learning paradigms. The contention results from a disagreement between the teacher and the model on what constitutes the salient features at the highest level of the symbol. There are four cases ZBT must handle, two of which result from the disagreement with the teacher. The parallel/serial dichotomy represents a vertical/horizontal tradeoff between the invariant and variant features of a domain. The resultant learned hierarchy allows ZBT to recognize structural differences while avoiding problems of exponential growth

DIVE in the cosmic web: voids with Delaunay Triangulation from discrete matter tracer distributions

We present a novel parameter-free cosmological void finder (\textsc{dive}, Delaunay TrIangulation Void findEr) based on Delaunay Triangulation (DT), which efficiently computes the empty spheres constrained by a discrete set of tracers. We define the spheres as DT voids, and describe their properties, including an universal density profile together with an intrinsic scatter. We apply this technique on 100 halo catalogues with volumes of 2.5\,$h^{-1}$Gpc side each, with a bias and number density similar to the BOSS CMASS Luminous Red Galaxies, performed with the \textsc{patchy} code. Our results show that there are two main species of DT voids, which can be characterised by the radius: they have different responses to halo redshift space distortions, to number density of tracers, and reside in different dark matter environments. Based on dynamical arguments using the tidal field tensor, we demonstrate that large DT voids are hosted in expanding regions, whereas the haloes used to construct them reside in collapsing ones. Our approach is therefore able to efficiently determine the troughs of the density field from galaxy surveys, and can be used to study their clustering. We further study the power spectra of DT voids, and find that the bias of the two populations are different, demonstrating that the small DT voids are essentially tracers of groups of haloes.Comment: 12 pages, 13 figure