AMSOM: Adaptive Moving Self-organizing Map for Clustering and Visualization

Spanakis, Gerasimos; Weiß, Gerhard

doi:10.5220/0005704801290140

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…Several parameters were used to initialize the SOM to obtain different models for each year and the final parameters used are given in Table 2. The model with the least quantization error was chosen as the best fit [48]. For this research, GeoSOM was also used to detect outliers, for sensitivity analysis of the parameters of the methods used, for the analysis of the U-matrix, and for component planes and for the final clustering.…”

Section: 3mentioning

confidence: 99%

Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps

Akande

Costa

Mateu

et al. 2017

Advances in Meteorology

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The explosion of data in the information age has provided an opportunity to explore the possibility of characterizing the climate patterns using data mining techniques. Nigeria has a unique tropical climate with two precipitation regimes: low precipitation in the north leading to aridity and desertification and high precipitation in parts of the southwest and southeast leading to large scale flooding. In this research, four indices have been used to characterize the intensity, frequency, and amount of rainfall over Nigeria. A type of Artificial Neural Network called the self-organizing map has been used to reduce the multiplicity of dimensions and produce four unique zones characterizing extreme precipitation conditions in Nigeria. This approach allowed for the assessment of spatial and temporal patterns in extreme precipitation in the last three decades. Precipitation properties in each cluster are discussed. The cluster closest to the Atlantic has high values of precipitation intensity, frequency, and duration, whereas the cluster closest to the Sahara Desert has low values. A significant increasing trend has been observed in the frequency of rainy days at the center of the northern region of Nigeria.

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Section: 3mentioning

confidence: 99%

Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps

Akande

Costa

Mateu

et al. 2017

Advances in Meteorology

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“…Self Organizing Map (SOM) adalah model jaringan saraf dengan pelatihannya unsupervised yang secara efektif dapat memetakan data berdimensi tinggi menjadi ruang berdimensi rendah (biasanya 2 dimensi) (Spanakis & Weiss, 2016). Jaringan SOM terdiri dari 2 lapisan yaitu lapisan input X dan lapisan output Y (neuron) dengan bobot W yang menghubungkan antara kedua lapisan tersebut.…”

Section: Self Organizing Map (Som) Clusteringunclassified

Peningkatan Performa Pengelompokan Pola Berpikir Siswa dalam Belajar pada Media Pembelajaran Menggunakan Direct Batch Growing Self Organizing Map

Izzuddin

Supianto²,

Tibyani³

et al. 2022

JTIIK

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Tidak sedikit siswa mengalami kendala untuk keluar dari kebuntuan berpikir saat belajar. Setiap siswa memiliki caranya masing-masing untuk menyelesaikan masalah kebuntuan tersebut, yang disebabkan oleh pola berpikir yang berbeda-beda. Kendati berbeda, pola berpikir tersebut memiliki kemiripan yang dapat dikelompokkan agar pemberian umpan balik dapat dilakukan dengan tepat secara berkelompok. Salah satu cara yang dapat dilakukan untuk mengelompokkan siswa berdasarkan pola berpikirnya adalah clustering. Penelitian untuk pengelompokkan berdasarkan kecerdasan sudah pernah dilakukan menggunakan salah satu teknik clustering yaitu Self Organizing Map (SOM). Namun SOM memiliki keterbatasan dalam menentukan ukuran jaringan karena bersifat statis. Keterbatasan yang ada pada SOM dapat diatasi, penelitian ini mengusulkan Direct Batch Growing Self Organizing Map (DBGSOM) yang bersifat dinamis dalam ukuran jaringan dan lebih cepat dalam proses pelatihannya. Penelitian ini dimulai dengan mengidentifikasi masalah untuk mengetahui kemungkinan penyelesaian permasalahan. Tahap selanjutnya adalah pengumpulan data dan pemilihan data yang digunakan dalam penelitian. Tahap akhir, evaluasi dilakukan terhadap data yang terdiri dari 12 assignment untuk mengetahui performa terbaik dari DBGSOM. Hasil evaluasi yang telah dilakukan menunjukkan bahwa clustering DBGSOM memperoleh performa lebih baik daripada SOM pada 11 assignment dari 12 assignment. Pengukuran signifikansi perbandingan dilakukan dengan metode Wilcoxon yang menghasilkan nilai test stat 8 dan critical value 13. Hal ini membuktikan bahwa penerapan DBGSOM mampu memberikan peningkatkan performa clustering yang signifikan dari SOM. AbstractA few times, students have difficulty getting out of the deadlock in thinking while studying. Each student has their own way of solving the deadlock problem, which is caused by different thinking patterns. Although different, these thinking patterns have similarities that can be grouped so that giving feedback can be done appropriately in groups. One way that can be done to group students based on their thinking patterns is clustering. Research for grouping based on intelligence has been done using one of the clustering techniques, namely Self Organizing Map (SOM). However, SOM has limitations in determining network size because it is static. The limitations that exist in SOM can be overcome, this study proposes a Direct Batch Growing Self Organizing Map (DBGSOM) which is dynamic in network size and faster in the training process. This research begins by identifying the problem to determine the possibility of solving the problem. The next stage is data collection and data selection used in research. The final stage, evaluation is carried out on the data consisting of 12 assignments to find out the best performance of DBGSOM. The results of the evaluation that have been carried out show that DBGSOM clustering has better performance than SOM on 11 assignments out of 12 assignments. The comparison significance measurement was carried out using the Wilcoxon method which resulted in a test stat value of 8 and a critical value of 13. This proves that the application of DBGSOM is able to provide a significant increase in clustering performance from SOM.

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A supervised method to enhance distance-based neural network clustering performance by discovering perfect representative neurons

Albathan

2023

Granul. Comput.

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Distance-based neural network clustering requires the 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 network with $$m\times N$$ m × N neurons. These neurons represent m subnetworks, and each subnetwork perfectly represents a part of the training set, where the clustering qualification indicators (the purity, normalized mutual information, and adjusted rand index measures) all equal 1. The results are statistically validated with a t test, and we demonstrate that the TDSM performs better than the original clustering paradigm on some real datasets.

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AMSOM: Adaptive Moving Self-organizing Map for Clustering and Visualization

Cited by 5 publications

References 31 publications

Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps

Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps

Peningkatan Performa Pengelompokan Pola Berpikir Siswa dalam Belajar pada Media Pembelajaran Menggunakan Direct Batch Growing Self Organizing Map

A supervised method to enhance distance-based neural network clustering performance by discovering perfect representative neurons

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