Clustering Ensemble Model Based on Self-Organizing Map Network

Hua, Wenqi; Mo, Lingfei

doi:10.1155/2020/2971565

Cited by 9 publications

(2 citation statements)

References 35 publications

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“…For the external method, as pointed out in (Lampinen and Oja, 1992), multiple-layer SOM clusters match the desired classes better than direct SOM clusters. Cascaded SOM (Hua and Mo, 2020) utilizes ensemble learning to get a more robust network structure by training the same samples multiple times and getting multiple weight vector matrixes, which will be used as the input for further layers' SOM training, then generating a final decision by learning responses of different clusters.…”

Section: Related Studiesmentioning

confidence: 99%

A Supervised Method to Enhance Distance-based Neural Networks' Clustering Performance by Discovering Perfect Representative Neurons

Albathan

2023

Preprint

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Distance-based neural network clustering has an intrinsic assumption that a particular neuron in the network represents a cluster centroid. However, not all these neurons can perfectly represent the training data; these neurons can only represent part of the training samples. This paper proposes an effective training data splitting method (TDSM) to find perfect representative neurons and improve the clustering results in a distance-based neutral network without changing the original network's internal algorithm or the training data quality. The method allows a network with N neurons to be enlarged to a new m×N neurons network. These neurons represent m sub-networks, and each sub-network perfectly represents a part of the training set with the clustering qualification indicators (purity, Normalized Mutual Information, and Adjusted Rand Index), all equalling 1. Results are statistically validated with a t-test, and we demonstrate that TDSM performs better than the original clustering paradigm on some real data sets.

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Section: Related Studiesmentioning

confidence: 99%

A Supervised Method to Enhance Distance-based Neural Networks' Clustering Performance by Discovering Perfect Representative Neurons

Albathan

2023

Preprint

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“…In the GG model, the height/width ratio suitable for the data can be determined automatically. The accumulated statistical measures, instead of the quantization error and topographic error, are used to determine where to insert new rows or columns in the map (Hua & Mo, 2020; Vesanto & Alhoniemi, 2000). Since the parameters in the model are constant, there is no need to set the total number of adaptation steps in advance.…”

Section: Related Workmentioning

confidence: 99%

An approach for constructing expert yellow pages for community question answering sites

et al. 2021

Expert Systems

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The rapid increase in the number of community-based question-and-answer services is attracting many users. Questions are posted and answered by community members. These users, who can help other users answer questions, can be considered experts. To facilitate finding a suitable expert and alleviate information overload, in this paper, expert yellow pages (EYP) for community question answering (CQA) are constructed. Considering the various lengths of texts, the biterm topic model (BTM) is used to model questions and fields of expertise. Then, two-dimensional EYP (2DEYP), which are composed of expertise field dimensions and question dimensions, are constructed. The intersections represent the cluster of experts. The proposed 2DEYP can be expanded both laterally and vertically for a more in-depth understanding and a more precise location. As the closer neurons represent similar topics, a novel labelling method is proposed to identify topic words for navigation. The method uses the distance between neurons as the differentiation capability. To further distinguish experts, a ranking mechanism is proposed. The experts can be ranked by integrating their expertise and activity levels. The expertise level is novel and characterized by both breadth and depth aspects. The proposed approach is evaluated via a real dataset, and the experimental results show that the proposed algorithm is feasible and performs well.artificial neural network, community question answering, expert yellows pages, knowledge management 1 | INTRODUCTION Community question answering (CQA) websites, such as Yahoo! Answers 1 and Zhihu, 2 are typical applications of Web 2.0 technology focusing on knowledge sharing. Community members seeking help post questions, and others share their knowledge by answering these questions (Yang et al., 2019). Because of their convenience, CQA websites attract many users. For example, Zhihu had more than 220 million users in November 2018. 3 Most of these users were expert users who could answer questions. The definition of an expert in CQA is much broader in that each member may have a higher level of expertise in a certain knowledge area (Liu,

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Primary Healthcare Model for Remote Area Using Self‐Organizing Map Network

Das

Sil

2021

Computational Intelligence and Healthcare Informatics

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Clustering Ensemble Model Based on Self-Organizing Map Network

Cited by 9 publications

References 35 publications

A Supervised Method to Enhance Distance-based Neural Networks' Clustering Performance by Discovering Perfect Representative Neurons

A Supervised Method to Enhance Distance-based Neural Networks' Clustering Performance by Discovering Perfect Representative Neurons

An approach for constructing expert yellow pages for community question answering sites

Primary Healthcare Model for Remote Area Using Self‐Organizing Map Network

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