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Flow field characterization and evaluation method based on unsupervised machine learning

JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING 2022年第PartA期215卷

作者： Li, Shanshan Feng, Qihong Zhang, Xianmin Liu, Haicheng Liu, Lijie Huang, Yingsong China Univ Petr East China Sch Petr Engn Qingdao 266580 Peoples R China Shengli Oilfield Co Res Inst Explorat & Dev SINOPEC Dongying 257015 Shandong Peoples R China

Long-term water flooding can be inefficient due to imbalanced displacement, resulting in sub-optimal recoveries. Therefore, the quantitative characterization and evaluation method of the flow field is significant for further development and adjustment. In this study, based on streamline simulation results, decoding rules were formulated to extract streamline data. Considering the attribute feature of each node along the streamline, flow field characterization indices were constructed. And combining the fast density peaks clustering (FDPC) algorithm and silhouette coefficient (SC), a novel flow field evaluation method based on streamline clustering was proposed. Results indicated that the invalid water circulation channels and areas with developing potential in reservoirs can be identified. Besides, the distribution of flow field can also be observed, and the characteristics of the identified region can be analyzed, which provides a basis for the subsequent flow field adjustment. Meanwhile, streamlines between injection and production wells can be subdivided to describe the distribution of water displacement capacity. Based on the evaluation results of streamline clustering, Gudao oilfield adjusted the flow field through injection-production adjustment, which effectively improved the development effect of the block and increased the recovery degree from 50.3% to 52.4%. This work provides an effective evaluation method to directly characterize the displacement changes for underground flow field, and it is of great significance for the decision-making of water flooding structure adjustment and optimization.

关键词： Water flooding Flow field characterization and evaluation Streamline characteristic Streamline clustering cluster algorithm Flow field adjustment Streamline simulation

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GenClust:: A genetic algorithm for clustering gene expression data -: art. no. 289

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BMC BIOINFORMATICS 2005年第1期6卷 289-289页

作者： Di Gesú, V Giancarlo, R Lo Bosco, G Raimondi, A Scaturro, D Univ Palermo Dipartimento Matemat & Applicaz I-90123 Palermo Italy

Background: clustering is a key step in the analysis of gene expression data, and in fact, many classical clustering algorithms are used, or more innovative ones have been designed and validated for the task. Despite the widespread use of artificial intelligence techniques in bioinformatics and, more generally, data analysis, there are very few clustering algorithms based on the genetic paradigm, yet that paradigm has great potential in finding good heuristic solutions to a difficult optimization problem such as clustering. Results: GenClust is a new genetic algorithm for clustering gene expression data. It has two key features: (a) a novel coding of the search space that is simple, compact and easy to update;(b) it can be used naturally in conjunction with data driven internal validation methods. We have experimented with the FOM methodology, specifically conceived for validating clusters of gene expression data. The validity of GenClust has been assessed experimentally on real data sets, both with the use of validation measures and in comparison with other algorithms, i.e., Average Link, Cast, Click and K-means. Conclusion: Experiments show that none of the algorithms we have used is markedly superior to the others across data sets and validation measures;i.e., in many cases the observed differences between the worst and best performing algorithm may be statistically insignificant and they could be considered equivalent. However, there are cases in which an algorithm may be better than others and therefore worthwhile. In particular, experiments for GenClust show that, although simple in its data representation, it converges very rapidly to a local optimum and that its ability to identify meaningful clusters is comparable, and sometimes superior, to that of more sophisticated algorithms. In addition, it is well suited for use in conjunction with data driven internal validation measures and, in particular, the FOM methodology.

关键词： cluster algorithm Internal Variance True Solution cluster Solution Rand Index

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Evaluating the efficiency of China's healthcare service: A weighted DEA-game theory in a competitive environment

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JOURNAL OF CLEANER PRODUCTION 2020年 270卷 122431-122431页

作者： Sun Yaya Chen Xi Zhou Xiaoyang Zhang Meixia Xidian Univ Sch Econ & Management Xian 710126 Peoples R China Xi An Jiao Tong Univ Sch Life Sci & Technol Xian 710049 Peoples R China Fourth Mil Med Univ Xijing Hosp Nursing Dept Xian 710032 Peoples R China

In the field of healthcare industries, reasonable and sound resource allocation, as well as its well-balanced distribution do not impose any encumbrance on a society's healthcare system, preventing negative effects induced by cost-ineffective therapies as well as resource-consuming treatments, upon which the key motivations of our research is constituted. We establish an ingenious and effective model by combining weighted Data Envelopment Analysis (DEA) and game theory to improve the quality and management of the healthcare. First, we aim to divide the decision-making units (DMUs) into group with homogeneity by k-means and canopy algorithm. Then, a model based on the weighted DEA-game theory is built to evaluate the healthcare service efficiency of each cluster. Last but not least, an empirical study is accordingly conducted. Analytical results show that our method outperforms the traditional DEA, throughout which three advantageous findings are therefore summarized as the key contributions of our research in terms of imbalance, healthcare service efficiency and its performance in coastal areas, thereby offering corresponding guidance and suggestions. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Healthcare service cluster algorithm Data envelopment analysis(DEA) Cross-efficiency Game theory

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A Novel Model Based on Deep Learning Approach Combining Data Decomposition Technique and Grouping Distribution Strategy for Water Demand Forecasting of Urban Users

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2024年第1期33卷 2450007-2450007页

作者： Cao, Zhang Yan, Hua Wu, Zhengping Li, Dong Wen, Bin China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China Univ Shanghai Sci & Technol Sch Energy & Power Engn Shanghai 200093 Peoples R China Shenzhen Xingyuan Intelligent Instrument Co Ltd Shenzhen 518000 Peoples R China

Urban user water demand prediction (WDP) is of significant importance for smart water supply system, which can provide a strong decision-making basis for the dispatching and management of smart water supply system. However, owing to the fluctuation, intermittence and nonstationarity of the user's water consumption in urban buildings, it is extremely difficult to predict accurately. Therefore, a novel short-term WDP model (Singular Spectrum Analysis Convolutional Neural Network Bidirectional Gate Recurrent Unit, SSA-CNN-BiGRU) is proposed to promote the stability and accuracy of WDP, which successfully introduces organic combinations including deep learning, decomposition technique, and data partitioning policies into the domain of WDP. First, raw data are decomposed into components that carry distinct frequency signals for weakening its nonstationarity and complexity. Then, all the components are automatically divided into several groups using clustering algorithm based on their entropy, after which deep learning method is adopted to predict by groups. Finally, the predicted result of each group is summed up to be fused as the final value. To validate the predictive performance of SSA-CNN-BiGRU, real data have been selected for this study. In experiments, SSA-CNN-BiGRU achieved a fitting of 94.73%. Comparison by relevant evaluation metrics demonstrates that the proposed model exhibits superior performance, thus providing a more accurate basis for WDP.

关键词： Urban user water demand forecasting deep learning data distribution strategy cluster algorithm convolutional neural network bidirectional gated recurrent unit

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A Global Solar Radiation Forecasting System Using Combined Supervised and Unsupervised Learning Models

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ENERGIES 2023年第23期16卷 7693页

作者： Wei, Chih-Chiang Yang, Yen-Chen Natl Taiwan Ocean Univ Dept Marine Environm Informat & Ctr Excellence Oce Keelung 20224 Taiwan

One of the most important sources of energy is the sun. Taiwan is located at a 22-25 degrees north latitude. Due to its proximity to the equator, it experiences only a small angle of sunlight incidence. Its unique geographical location can obtain sustainable and stable solar resources. This study uses research on solar radiation forecasts to maximize the benefits of solar power generation, and it develops methods that can predict future solar radiation patterns to help reduce the costs of solar power generation. This study built supervised machine learning models, known as a deep neural network (DNN) and a long-short-term memory neural network (LSTM). A hybrid supervised and unsupervised model, namely a cluster-based artificial neural network (k-means clustering- and fuzzy C-means clustering-based models) was developed. After establishing these models, the study evaluated their prediction results. For different prediction periods, the study selected the best-performing model based on the results and proposed combining them to establish a real-time-updated solar radiation forecast system capable of predicting the next 12 h. The study area covered Kaohsiung, Hualien, and Penghu in Taiwan. Data from ground stations of the Central Weather Administration, collected between 1993 and 2021, as well as the solar angle parameters of each station, were used as input data for the model. The results of this study show that different models offer advantages and disadvantages in predicting different future times. The hybrid prediction system can predict future solar radiation more accurately than a single model.

关键词： solar radiation prediction cluster algorithm neural network

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Nearest Neighbor Networks: clustering expression data based on gene neighborhoods

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BMC BIOINFORMATICS 2007年第1期8卷 250-250页

作者： Huttenhower, Curtis Flamholz, Avi I. Landis, Jessica N. Sahi, Sauhard Myers, Chad L. Olszewski, Kellen L. Hibbs, Matthew A. Siemers, Nathan O. Troyanskaya, Olga G. Coller, Hilary A. Princeton Univ Dept Mol Biol Princeton NJ 08544 USA Princeton Univ Dept Comp Sci Princeton NJ 08544 USA Princeton Univ Lewis Sigler Inst Integrat Genom Princeton NJ 08544 USA Bristol Myers Squibb Co Pennington NJ 08534 USA

Background: The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both individual biological pathways and the integrated workings of the cell. However, translating this amount of data into biological insight remains a daunting task. An important initial step in the analysis of microarray data is clustering of genes with similar behavior. A number of classical techniques are commonly used to perform this task, particularly hierarchical and K-means clustering, and many novel approaches have been suggested recently. While these approaches are useful, they are not without drawbacks;these methods can find clusters in purely random data, and even clusters enriched for biological functions can be skewed towards a small number of processes ( e. g. ribosomes). Results: We developed Nearest Neighbor Networks ( NNN), a graph-based algorithm to generate clusters of genes with similar expression profiles. This method produces clusters based on overlapping cliques within an interaction network generated from mutual nearest neighborhoods. This focus on nearest neighbors rather than on absolute distance measures allows us to capture clusters with high connectivity even when they are spatially separated, and requiring mutual nearest neighbors allows genes with no sufficiently similar partners to remain unclustered. We compared the clusters generated by NNN with those generated by eight other clustering methods. NNN was particularly successful at generating functionally coherent clusters with high precision, and these clusters generally represented a much broader selection of biological processes than those recovered by other methods. Conclusion: The Nearest Neighbor Networks algorithm is a valuable clustering method that effectively groups genes that are likely to be functionally related. It is particularly attractive due to its simplicity, its success in the analysis of large data

关键词： cluster algorithm Gene Pair Neighborhood Size Random Data Neighbor Network

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Shortest triplet clustering: reconstructing large phylogenies using representative sets

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BMC BIOINFORMATICS 2005年第1期6卷 92-92页

作者： Vinh, LS von Haeseler, A Heinrich-Heine-Universität Düsseldorf WE Informatik Universitätstr. 1 D-040225 Düsseldorf Germany Forschungszentrum Jülich Germany

Background: Understanding the evolutionary relationships among species based on their genetic information is one of the primary objectives in phylogenetic analysis. Reconstructing phylogenies for large data sets is still a challenging task in Bioinformatics. Results: We propose a new distance-based clustering method, the shortest triplet clustering algorithm (STC), to reconstruct phylogenies. The main idea is the introduction of a natural definition of so-called k- representative sets. Based on k-representative sets, shortest triplets are reconstructed and serve as building blocks for the STC algorithm to agglomerate sequences for tree reconstruction in O(n2) time for n sequences. Simulations show that STC gives better topological accuracy than other tested methods that also build a first starting tree. STC appears as a very good method to start the tree reconstruction. However, all tested methods give similar results if balanced nearest neighbor interchange (BNNI) is applied as a post-processing step. BNNI leads to an improvement in all instances. The program is available at http://***/software/stc/. Conclusion: The results demonstrate that the new approach efficiently reconstructs phylogenies for large data sets. We found that BNNI boosts the topological accuracy of all methods including STC, therefore, one should use BNNI as a post-processing step to get better topological accuracy.

关键词： Path Length cluster algorithm Average Path Length Stochastic Error Large Simulation

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Metric for measuring the effectiveness of clustering of DNA microarray expression

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BMC BIOINFORMATICS 2006年第Sup2期7卷 S5-S5页

作者： Loganantharaj, Raja Cheepala, Satish Clifford, John Univ Louisiana Bioinformat Res Lab Lafayette LA 70504 USA LSU Hlth Sci Ctr Dept Biochem & Mol Biol Shreveport LA 71130 USA Feist Weiller Canc Ctr Shreveport LA 71130 USA

Background: The recent advancement of microarray technology with lower noise and better affordability makes it possible to determine expression of several thousand genes simultaneously. The differentially expressed genes are filtered first and then clustered based on the expression profiles of the genes. A large number of clustering algorithms and distance measuring matrices are proposed in the literature. The popular ones among them include hierarchal clustering and k-means clustering. These algorithms have often used the Euclidian distance or Pearson correlation distance. The biologists or the practitioners are often confused as to which algorithm to use since there is no clear winner among algorithms or among distance measuring metrics. Several validation indices have been proposed in the literature and these are based directly or indirectly on distances;hence a method that uses any of these indices does not relate to any biological features such as biological processes or molecular functions. Results: In this paper we have proposed a metric to measure the effectiveness of clustering algorithms of genes by computing inter-cluster cohesiveness and as well as the intra-cluster separation with respect to biological features such as biological processes or molecular functions. We have applied this metric to the clusters on the data set that we have created as part of a larger study to determine the cancer suppressive mechanism of a class of chemicals called retinoids. We have considered hierarchal and k-means clustering with Euclidian and Pearson correlation distances. Our results show that genes of similar expression profiles are more likely to be closely related to biological processes than they are to molecular functions. The findings have been supported by many works in the area of gene clustering. Conclusion: The best clustering algorithm of genes must achieve cohesiveness within a cluster with respect to some biological features, and as well as maximum separati

关键词： Gene Ontology cluster algorithm Molecular Function Behavioral Group Saccharomyces Genome Database

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A methodology to assess the intrinsic discriminative ability of a distance function and its interplay with clustering algorithms for microarray data analysis

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BMC BIOINFORMATICS 2013年第Sup1期14卷 1-14页

作者： Giancarlo, Raffaele Lo Bosco, Giosue Pinello, Luca Utro, Filippo Univ Palermo Dipartimento Matemat & Informat I-90123 Palermo Italy Dana Farber Canc Inst Dept Biostat Boston MA 02115 USA Harvard Univ Sch Publ Hlth Boston MA 02115 USA IBM TJ Watson Res Ctr Computat Genom Grp Yorktown Hts NY 10598 USA

Background: clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from statistics to computer science. Following Handl et al., it can be summarized as a three step process: (1) choice of a distance function;(2) choice of a clustering algorithm;(3) choice of a validation method. Although such a purist approach to clustering is hardly seen in many areas of science, genomic data require that level of attention, if inferences made from cluster analysis have to be of some relevance to biomedical research. Results: A procedure is proposed for the assessment of the discriminative ability of a distance function. That is, the evaluation of the ability of a distance function to capture structure in a dataset. It is based on the introduction of a new external validation index, referred to as Balanced Misclassification Index (BMI, for short) and of a nontrivial modification of the well known Receiver Operating Curve (ROC, for short), which we refer to as Corrected ROC (CROC, for short). The main results are: (a) a quantitative and qualitative method to describe the intrinsic separation ability of a distance;(b) a quantitative method to assess the performance of a clustering algorithm in conjunction with the intrinsic separation ability of a distance function. The proposed procedure is more informative than the ones available in the literature due to the adopted tools. Indeed, the first one allows to map distances and clustering solutions as graphical objects on a plane, and gives information about the bias of the clustering algorithm with respect to a distance. The second tool is a new external validity index which shows similar performances with respect to the state of the art, but with more flexibility, allowing for a broader spectrum of applications. In fact, it allows not only to quantify the merit of each clustering solution but also to quantify the agglomerative or divisive errors due to the

关键词： cluster algorithm Distance Function cluster Solution Connectivity Matrix Gold Solution

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Noise reduction in protein-protein interaction graphs by the implementation of a novel weighting scheme

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BMC BIOINFORMATICS 2011年第1期12卷 1-10页

作者： Kritikos, George D. Moschopoulos, Charalampos Vazirgiannis, Michalis Kossida, Sophia Acad Athens Bioinformat & Med Informat Team Biomed Res Fdn GR-11527 Athens Greece Athens Univ Econ & Business Dept Informat GR-10434 Athens Greece Univ Patras Pattern Recognit Lab Dept Comp Engn & Informat GR-26500 Patras Rio Greece Ecole Telecom ParisTech Inst Telecom Dept Informat & Reseaux Paris France

Background: Recent technological advances applied to biology such as yeast-two-hybrid, phage display and mass spectrometry have enabled us to create a detailed map of protein interaction networks. These interaction networks represent a rich, yet noisy, source of data that could be used to extract meaningful information, such as protein complexes. Several interaction network weighting schemes have been proposed so far in the literature in order to eliminate the noise inherent in interactome data. In this paper, we propose a novel weighting scheme and apply it to the S. cerevisiae interactome. Complex prediction rates are improved by up to 39%, depending on the clustering algorithm applied. Results: We adopt a two step procedure. During the first step, by applying both novel and well established protein-protein interaction (PPI) weighting methods, weights are introduced to the original interactome graph based on the confidence level that a given interaction is a true-positive one. The second step applies clustering using established algorithms in the field of graph theory, as well as two variations of Spectral clustering. The clustered interactome networks are also cross-validated against the confirmed protein complexes present in the MIPS database. Conclusions: The results of our experimental work demonstrate that interactome graph weighting methods clearly improve the clustering results of several clustering algorithms. Moreover, our proposed weighting scheme outperforms other approaches of PPI graph weighting.

关键词： cluster algorithm Positive Predictive Value Weighting Scheme Spectral cluster Protein Interaction Network

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