A General Transfer Learning-based Gaussian Mixture Model for Clustering

Wang, Rongrong; Zhou, Jin; Jiang, Huiling; Han, Shiyuan; Wang, Lin; Wang, Dong; Chen, Yuehui

doi:10.1007/s40815-020-01016-3

Cited by 14 publications

(3 citation statements)

References 35 publications

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“…Cluster analysis can be performed using partitioning methods to separate data based on similarities and differences in terms of relevant features, density‐based methods with a focus on the spatial distribution of data, hierarchy‐based and grid‐based methods where clusters are identified at various layers of complexity within the data set, model‐based methods that use statistical methods or NNs and constraint‐based methods that incorporate domain knowledge [ 68 ]. Commonly used clustering algorithms (Table 2 ) include K‐means clustering [ 25 ] (distribution based), agglomerative hierarchical clustering [ 52 ] (hierarchy based), density‐based spatial clustering of applications with noise [ 65 ] (density based), Gaussian mixed model clustering [ 84 ] (distribution based) and deep NN architectures [ 67 ].…”

Section: Understanding the Toolbox Of Methods For Ai‐driven Researchmentioning

confidence: 99%

A practical guide to the implementation of artificial intelligence in orthopaedic research—Part 2: A technical introduction

Zsidai,

Kaarre,

Narup

et al. 2024

J. exp. orthop.

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Recent advances in artificial intelligence (AI) present a broad range of possibilities in medical research. However, orthopaedic researchers aiming to participate in research projects implementing AI‐based techniques require a sound understanding of the technical fundamentals of this rapidly developing field. Initial sections of this technical primer provide an overview of the general and the more detailed taxonomy of AI methods. Researchers are presented with the technical basics of the most frequently performed machine learning (ML) tasks, such as classification, regression, clustering and dimensionality reduction. Additionally, the spectrum of supervision in ML including the domains of supervised, unsupervised, semisupervised and self‐supervised learning will be explored. Recent advances in neural networks (NNs) and deep learning (DL) architectures have rendered them essential tools for the analysis of complex medical data, which warrants a rudimentary technical introduction to orthopaedic researchers. Furthermore, the capability of natural language processing (NLP) to interpret patterns in human language is discussed and may offer several potential applications in medical text classification, patient sentiment analysis and clinical decision support. The technical discussion concludes with the transformative potential of generative AI and large language models (LLMs) on AI research. Consequently, this second article of the series aims to equip orthopaedic researchers with the fundamental technical knowledge required to engage in interdisciplinary collaboration in AI‐driven orthopaedic research.Level of EvidenceLevel IV.

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Section: Understanding the Toolbox Of Methods For Ai‐driven Researchmentioning

confidence: 99%

A practical guide to the implementation of artificial intelligence in orthopaedic research—Part 2: A technical introduction

Zsidai,

Kaarre,

Narup

et al. 2024

J. exp. orthop.

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“…The Gaussian mixture model is commonly used for trajectory clustering based on an iterative algorithm. The probability is taken as the clustering criterion (Wang et al, 2021;Fu et al, 2021). From the perspective of good clustering results, its distribution of within-cluster similarity should also obey the Gaussian mixture function.…”

Section: State Of the Artmentioning

confidence: 99%

Unsupervised hierarchical methodology of maritime traffic pattern extraction for knowledge discovery

Lam

Yang

et al. 2022

Transportation Research Part C: Emerging Technologies

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“…Jiang et al proposed transfer spectral clustering (TSC), which could transfer knowledge from related clustering task [ 12 ]. Wang et al extended three traditional Gaussian mixture model (GMM) to transfer clustering versions [ 13 ]. These methods are more suitable for the clustering problem that has definite boundaries.…”

Section: Introductionmentioning

confidence: 99%

Sorting Center Value Identification of “Internet + Recycling” Based on Transfer Clustering

Cheng

Luan

2022

Sensors

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As the core link of the “Internet + Recycling” process, the value identification of the sorting center is a great challenge due to its small and imbalanced data set. This paper utilizes transfer fuzzy c-means to improve the value assessment accuracy of the sorting center by transferring the knowledge of customers clustering. To ensure the transfer effect, an inter-class balanced data selection method is proposed to select a balanced and more qualified subset of the source domain. Furthermore, an improved RFM (Recency, Frequency, and Monetary) model, named GFMR (Gap, Frequency, Monetary, and Repeat), has been presented to attain a more reasonable attribute description for sorting centers and consumers. The application in the field of electronic waste recycling shows the effectiveness and advantages of the proposed method.

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A General Transfer Learning-based Gaussian Mixture Model for Clustering

Cited by 14 publications

References 35 publications

A practical guide to the implementation of artificial intelligence in orthopaedic research—Part 2: A technical introduction

A practical guide to the implementation of artificial intelligence in orthopaedic research—Part 2: A technical introduction

Unsupervised hierarchical methodology of maritime traffic pattern extraction for knowledge discovery

Sorting Center Value Identification of “Internet + Recycling” Based on Transfer Clustering

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