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نتیجه جستجو - Hierarchical clustering

تعداد مقالات یافته شده: 19
ردیف عنوان نوع
1 Online energy management strategy of fuel cell hybrid electric vehicles based on rule learning
استراتژی مدیریت انرژی آنلاین از وسایل نقلیه برقی هیبریدی سلول سوختی بر اساس یادگیری قانون-2020
In this paper, a rule learning based energy management strategy is proposed to achieve preferable energy consumption economy for fuel cell hybrid electric vehicles. Firstly, the optimal control sequence of fuel cell power and the state of charge trajectory of lithium-ion battery pack during driving are derived offline by the Pontryagin’s minimum principle. Next, the K-means algorithm is employed to hierarchically cluster the optimal solution into the simplified data set. Then, the repeated incremental pruning to produce error reduction algorithm, as a propositional rule learning strategy, is leveraged to learn and classify the underlying rules. Finally, the multiple linear regression algorithm is applied to fit the abstracted parameters of generated rule set. Simulation results highlight that the proposed strategy can achieve more than 95% savings of energy consumption economy, solved by Pontryagin’s minimum principle, with less calculation intensity and without dependence on prior driving conditions, thereby manifesting the feasibility of online application.
Keywords: Fuel cell hybrid electric vehicle | Energy management strategy | Hierarchical clustering | Rule learning
مقاله انگلیسی
2 Exploration of the mechanism of traditional Chinese medicine by AI approach using unsupervised machine learning for cellular functional similarity of compounds in heterogeneous networks, XiaoErFuPi granules as an example
کاوش مکانیسم طب سنتی چینی با رویکرد هوش مصنوعی با استفاده از یادگیری ماشین بدون نظارت برای شباهت عملکردی سلولی ترکیبات در شبکه های ناهمگن ، گرانول های XiaoErFuPi به عنوان مثال-2020
‘Polypharmacology’ is usually used to describe the network-wide effect of a single compound, but traditional Chinese medicine (TCM) has a polypharmacological effect naturally based on the ‘multi-components, multitargets and multi-pathways’ principle. It is a challenge to investigate the polypharmacology mechanism of TCM with multiple components. In this study, we used XiaoErFuPi (XEFP) granules as an example to describe an unsupervised learning strategy for polypharmacology research of TCM and to explore the mechanism of XEFP polypharmacology against multifactorial disease function dyspepsia (FD). Unsupervised clustering of compounds based on similarity evaluation of cellular function fingerprints showed that compounds of TCM without similar targets and chemical structure could also exert similar therapeutic effects on the same disease, as different targets participate in the same pathway closely associated with the pathological process. In this study, we proposed an unsupervised machine learning strategy for exploring the polypharmacology-based mechanism of TCM, utilizing hierarchical clustering based on cellular functional similarity, to establish a connection from the chemical clustering module to cellular function. Meanwhile, FDA-approved drugs against FD were used as references for the mechanism of action (MoA) of FD. First, according to the compound-compound network built by the similarity of cellular function of XEFP compounds and FDA-approved FD drugs, the possible therapeutic function of TCM may represent a known mechanism of FDA-approved drugs. Then, as unsupervised learning, hierarchical clustering of TCM compounds based on cellular function fingerprint similarity could help to classify the compounds into several modules with similar therapeutic functions to investigate the polypharmacology effect of TCM. Furthermore, the integration of quantitative omics data of TCM and approved drugs (from LINCS datasets) provides more quantitative evidence for TCM therapeutic function consistency with approved drugs. A spasmolytic activity experiment was launched to confirm vanillic acid activity to repress smooth muscle contraction; vanillic acid was also predicted to be active compound of XEFP, supporting the accuracy of our strategy. In summary, the approach proposed in this study provides a new unsupervised learning strategy for polypharmacological research investigating TCM by establishing a connection between the compound functional module and drug-activated cellular processes shared with FDA-approved drugs, which may elucidate the unique mechanism of traditional medicine using FDA-approved drugs as references, facilitate the discovery of potential active compounds of TCM and provide new insights into complex diseases.
Keywords: Polypharmacology | Traditional Chinese medicine | Unsupervised clustering | Cellular function fingerprints | FDA-approved drugs | Functional dyspepsia
مقاله انگلیسی
3 Fast and effective Big Data exploration by clustering
اکتشاف سریع و موثر داده های بزرگ با خوشه بندی-2020
The rise of Big Data era calls for more efficient and effective Data Exploration and analysis tools. In this respect, the need to support advanced analytics on Big Data is driving data scientist’ interest toward massively parallel distributed systems and software platforms, such as Map-Reduce and Spark, that make possible their scalable utilization. However, when complex data mining algorithms are required, their fully scalable deployment on such platforms faces a number of technical challenges that grow with the complexity of the algorithms involved. Thus algorithms, that were originally designed for a sequential nature, must often be redesigned in order to effectively use the distributed computational resources. In this paper, we explore these problems, and then propose a solution which has proven to be very effective on the complex hierarchical clustering algorithm CLUBS+. By using four stages of successive refinements, CLUBS+ delivers high-quality clusters of data grouped around their centroids, working in a totally unsupervised fashion. Experimental results confirm the accuracy and scalability of CLUBS+ on platforms tailored for Big Data management.
Keywords: Big Data | Clustering | Data exploration
مقاله انگلیسی
4 Combining hierarchical clustering approaches using the PCA method
ترکیب روشهای خوشه بندی سلسله مراتبی با استفاده از روش PCA-2019
In expert systems, data mining methods are algorithms that simulate humans’ problem-solving capabil- ities. Clustering methods as unsupervised machine learning methods are crucial approaches to catego- rize similar samples in the same categories. The use of different clustering algorithms to a given dataset produces clusters with different qualities. Hence, many researchers have applied clustering combination methods to reduce the risk of choosing an inappropriate clustering algorithm. In these methods, the out- puts of several clustering algorithms are combined. In these research works, the input hierarchical clus- terings are transformed to descriptor matrices and their combination is achieved by aggregating their descriptor matrices. In previous works, only element-wise aggregation operators have been used and the relation between the elements of each descriptor matrix has been ignored. However, the value of each element of the descriptor matrix is meaningful in comparison with its other elements. The current study proposes a novel method of combining hierarchical clustering approaches based on principle component analysis (PCA). PCA as an aggregator allows considering all elements of the descriptor matrices. In the proposed approach, basic clusters are made and transformed to descriptor matrices. Then, a final ma- trix is extracted from the descriptor matrices using PCA. Next, a final dendrogram is constructed from the matrix that is used to summarize the results of the diverse clustering. The experimental results on popular available datasets show the superiority of the clustering accuracy of the proposed method over basic clustering methods such as single, average and centroid linkage and previously combined hierar- chical clustering methods. In addition, statistical tests show that the proposed method significantly out- performed hierarchical clustering combination methods with element-wise averaging operators in almost all tested datasets. Several experiments have also been conducted which confirm the robustness of the proposed method for its parameter setting.
Keywords: Clustering | Hierarchical clustering | Principle component analysis | PCA
مقاله انگلیسی
5 Identification and analysis of behavioral phenotypes in autism spectrum disorder via unsupervised machine learning
شناسایی و تجزیه و تحلیل فنوتیپ های رفتاری در اختلال طیف اوتیسم از طریق یادگیری ماشین بدون نظارت-2019
Background and objective: Autism spectrum disorder (ASD) is a heterogeneous disorder. Research has explored potential ASD subgroups with preliminary evidence supporting the existence of behaviorally and genetically distinct subgroups; however, research has yet to leverage machine learning to identify phenotypes on a scale large enough to robustly examine treatment response across such subgroups. The purpose of the present study was to apply Gaussian Mixture Models and Hierarchical Clustering to identify behavioral phenotypes of ASD and examine treatment response across the learned phenotypes. Materials and methods: The present study included a sample of children with ASD (N = 2400), the largest of its kind to date. Unsupervised machine learning was applied to model ASD subgroups as well as their taxonomic relationships. Retrospective treatment data were available for a portion of the sample (n =1034). Treatment response was examined within each subgroup via regression. Results: The application of a Gaussian Mixture Model revealed 16 subgroups. Further examination of the subgroups through Hierarchical Agglomerative Clustering suggested 2 overlying behavioral phenotypes with unique deficit profiles each composed of subgroups that differed in severity of those deficits. Furthermore, differentiated response to treatment was found across subtypes, with a substantially higher amount of variance accounted for due to the homogenization effect of the clustering. Discussion: The high amount of variance explained by the regression models indicates that clustering provides a basis for homogenization, and thus an opportunity to tailor treatment based on cluster memberships. These findings have significant implications on prognosis and targeted treatment of ASD, and pave the way for personalized intervention based on unsupervised machine learning.
Keywords: Machine learning | Autism spectrum disorder | Behavioral phenotypes | Cluster analysis | Treatment response
مقاله انگلیسی
6 Unsupervised classification of multi-omics data during cardiac remodeling using deep learning
طبقه بندی بدون نظارت شده داده های چند omics در طی بازسازی قلب با استفاده از یادگیری عمیق-2019
Integration of multi-omics in cardiovascular diseases (CVDs) presents high potentials for translational discoveries. By analyzing abundance levels of heterogeneous molecules over time, we may uncover biological interactions and networks that were previously unidentifiable. However, to effectively perform integrative analysis of temporal multi-omics, computational methods must account for the heterogeneity and complexity in the data. To this end, we performed unsupervised classification of proteins and metabolites in mice during cardiac remodeling using two innovative deep learning (DL) approaches. First, long short-term memory (LSTM)- based variational autoencoder (LSTM-VAE) was trained on time-series numeric data. The low-dimensional embeddings extracted from LSTM-VAE were then used for clustering. Second, deep convolutional embedded clustering (DCEC) was applied on images of temporal trends. Instead of a two-step procedure, DCEC performes a joint optimization for image reconstruction and cluster assignment. Additionally, we performed K-means clustering, partitioning around medoids (PAM), and hierarchical clustering. Pathway enrichment analysis using the Reactome knowledgebase demonstrated that DL methods yielded higher numbers of significant biological pathways than conventional clustering algorithms. In particular, DCEC resulted in the highest number of enriched pathways, suggesting the strength of its unified framework based on visual similarities. Overall, unsupervised DL is shown to be a promising analytical approach for integrative analysis of temporal multi-omics.
Keywords: Cardiovascular | Clustering | Multi-omics Time-series | Unsupervised deep learning | Integrative analysis
مقاله انگلیسی
7 Ontology-based adaptive testing for automated driving functions using data mining techniques
آزمایش تطبیقی ​​مبتنی بر هستی شناسی برای عملکردهای رانندگی خودکار با استفاده از تکنیک داده کاوی-2019
This paper presents an adaptive verification framework for automated driving functions based on ontologies and data-mining techniques. Despite the recent rapid growth of driver assistance systems to consolidate road safety, they still have various challenges coping with dynamic traffic situations of daily life. Therefore, automotive systems engineering has established data- and knowledge-driven test methods to assure the required functional safety and reliability in a highly safety-critical context. However, the reliance on field testing is inadequate and, in particular, time- and cost-intensive when applied to the next generation of automated driving functions, e.g. collision-free emergency braking and vehicle platooning. The presented framework utilises an ontology-based test scenario synthesis to identify criticality margins using a Hardware-in-the-Loop co-simulation platform for automated driving functions. Additionally, we demonstrate a systematic process to complement virtual testing by extracting insights from field testing database using eventbased time-series analysis. To this end, data mining techniques are used to obtain representative scenarios witnessed in real-world traffic. Agglomerative hierarchical clustering is performed to extract homogeneous groups (clusters) from recorded triggering events by proximity metrics using normalised cross-correlations. Extracted scenarios are subsequently used at earlier stages of development to effectively and efficiently ensure reliability and safety. In summary, the results show the benefits and some of the challenges of using the industry-proven framework, which enables a cost-effective extension of test domain vaidility throughout software product engineering.
Keywords: Event-based time-series analysis | Data mining | Hierarchical agglomerative clustering | Ontology-based test scenario synthesis | Hardware-in-the-Loop co-simulation | platform
مقاله انگلیسی
8 A correlation analysis of information use, social networks and cooperation consciousness in travel behaviors
تجزیه و تحلیل همبستگی استفاده از اطلاعات ، شبکه های اجتماعی و آگاهی همکاری در رفتارهای مسافرتی-2019
The rapid development of information and communication technologies, the availability of various traveling information, and the wide use of social networks facilitate the selection of the appropriate mode of transport in urban traveling. To better understand such selection processes, this paper presents a hierarchical clustering analysis of the transport data collected from some major cities in China for exploring the relationship between information use, social networks, and cooperation consciousness of individual travelers. This leads to the identification of three underlying patterns of cooperation consciousness behaviors including the pro-social relation, the diversity average relation and the homogeneous random relation. An analysis of such patterns shows that there is much to be done for enhancing the social and environmental awareness of travelers in individual travel choices for the sustainable development of urban traveling. This study contributes to the transportation study through the provision of better understanding of the influence of information use and social networks on conscious cooperation behaviors in urban traveling. The findings are beneficial for developing a sustainable travel culture through information sharing and conscious cooperation between and among travelers.
Keywords: Transportation | Travel behavior | Information use | Social networks | Cooperation consciousness | Clustering analysis | Pattern recognition
مقاله انگلیسی
9 Automatic trajectory recognition in Active Target Time Projection Chambers data by means of hierarchical clustering
به رسمیت شناختن مسیر خودکار در داده های زمان طرح ریزی زمان فعال هدف با استفاده از خوشه بندی سلسله مراتبی-2019
The automatic reconstruction of three-dimensional particle tracks from Active Target Time Projection Chambers data can be a challenging task, especially in the presence of noise. In this article, we propose a non-parametric algorithm that is based on the idea of clustering point triplets instead of the original points. We define an appropriate distance measure on point triplets and then apply a single-link hierarchical clustering on the triplets. Compared to parametric approaches like RANSAC or the Hough transform, the new algorithm has the advantage of potentially finding trajectories even of shapes that are not known beforehand. This feature is particularly important in low-energy nuclear physics experiments with Active Targets operating inside a magnetic field. The algorithm has been validated using data from experiments performed with the Active Target Time Projection Chamber developed at the National Superconducting Cyclotron Laboratory (NSCL). The results demonstrate the capability of the algorithm to identify and isolate particle tracks that describe non-analytical trajectories. For curved tracks, the vertex detection recall was 86% and the precision 94%. For straight tracks, the vertex detection recall was 96% and the precision 98%. In the case of a test set containing only straight linear tracks, the algorithm performed better than an iterative Hough transform.
Keywords: Time Projection Chambers | Active Target | Pattern recognition | Clustering
مقاله انگلیسی
10 Clustering suicides: A data-driven, exploratory machine learning approach
خودکشی های خوشه ای: یک رویکرد یادگیری ماشین اکتشافی مبتنی بر داده ها-2019
Methods of suicide have received considerable attention in suicide research. The common approach to differentiate methods of suicide is the classification into “violent” versus “non-violent” method. Interestingly, since the proposition of this dichotomous differentiation, no further efforts have been made to question the validity of such a classification of suicides. This study aimed to challenge the traditional separation into “violent” and “non-violent” suicides by generating a cluster analysis with a data-driven, machine learning approach. In a retrospective analysis, data on all officially confirmed suicides (N = 77,894) in Austria between 1970 and 2016 were assessed. Based on a defined distance metric between distributions of suicides over age group and month of the year, a standard hierarchical clustering method was performed with the five most frequent suicide methods. In cluster analysis, poisoning emerged as distinct from all other methods – both in the entire sample as well as in the male subsample. Violent suicides could be further divided into sub-clusters: hanging, shooting, and drowning on the one hand and jumping on the other hand. In the female sample, two different clusters were revealed – hanging and drowning on the one hand and jumping, poisoning, and shooting on the other. Our datadriven results in this large epidemiological study confirmed the traditional dichotomization of suicide methods into “violent” and “non-violent” methods, but on closer inspection “violent methods” can be further divided into sub-clusters and a different cluster pattern could be identified for women, requiring further research to support these refined suicide phenotypes.
Keywords: Suicide | Suicide methods | Machine-learning | Violent suicide | Cluster analysis
مقاله انگلیسی
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