An Experimental Study on Asymmetric Self-Organizing Map

Olszewski, Dominik

doi:10.1007/978-3-642-23878-9_6

Cited by 9 publications

(12 citation statements)

References 8 publications

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“…Establishing a well-grounded and theoretically justified combination of SOM and k-means requires utilizing the same (dis)similarity measure. And since it has been proven in [10] and in [11] that the asymmetric SOM is superior over its symmetric counterpart, we claim that the marriage of asymmetric SOM and asymmetric k-means (especially in our enhanced version) is a promising and justified idea.…”

Section: Justification Of the Two Proposed Methodsmentioning

confidence: 80%

“…The asymmetric SOM from [10] was later extended in [11], where the justification of the method was proved to be applicable in a wider research spectrum than only textual data analysis.…”

Section: Related Workmentioning

confidence: 99%

“…The energy function (8) can be optimized in the similar way as the error function (4). For detailed information, see [38] or [11].…”

Section: Asymmetric Self-organizing Mapmentioning

confidence: 99%

“…Therefore, the SOM algorithm allows for visualization of relationships between objects in multidimensional space. The asymmetric version of the SOM algorithm was introduced in [10], and the justification of the asymmetric approach was extended in [11].…”

Section: Introductionmentioning

confidence: 99%

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Asymmetric $$k$$ k -Means Clustering of the Asymmetric Self-Organizing Map

Olszewski

2015

Neural Process Lett

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An asymmetric approach to clustering of the asymmetric self-organizing map is proposed. The clustering is performed using an improved asymmetric version of the well-known k-means algorithm. The improved asymmetric k-means algorithm is the second proposal of this paper. As a result, we obtain a two-stage fully asymmetric data analysis technique. In this way, we maintain the methodological consistency of the both utilized methods, because they are both formulated in asymmetric versions, and consequently, they both properly adjust to asymmetric relationships in analyzed data. The results of our experiments on real data confirm the effectiveness of the proposed approach.

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Section: Justification Of the Two Proposed Methodsmentioning

confidence: 80%

“…The asymmetric SOM from [10] was later extended in [11], where the justification of the method was proved to be applicable in a wider research spectrum than only textual data analysis.…”

Section: Related Workmentioning

confidence: 99%

“…The energy function (8) can be optimized in the similar way as the error function (4). For detailed information, see [38] or [11].…”

Section: Asymmetric Self-organizing Mapmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Asymmetric $$k$$ k -Means Clustering of the Asymmetric Self-Organizing Map

Olszewski

2015

Neural Process Lett

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“…Although, the SOM is an originally unsupervised algorithm there exist supervised extensions [9], [14], [13], [7]. The SOM has been proved as an efficient data mining tool in many real life applications [11], [12], [18], [19]. In this paper, we focus on using the labeled SOM model for mapping the RF used in classification tasks.…”

Section: Introductionmentioning

confidence: 99%

Visualizing Random Forest with Self-Organising Map

Płoński

Заремба

2014

Artificial Intelligence and Soft Computing

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Abstract. Random Forest (RF) is a powerful ensemble method for classification and regression tasks. It consists of decision trees set. Although, a single tree is well interpretable for human, the ensemble of trees is a black-box model. The popular technique to look inside the RF model is to visualize a RF proximity matrix obtained on data samples with Multidimensional Scaling (MDS) method. Herein, we present a novel method based on Self-Organising Maps (SOM) for revealing intrinsic relationships in data that lay inside the RF used for classification tasks. We propose an algorithm to learn the SOM with the proximity matrix obtained from the RF. The visualization of RF proximity matrix with MDS and SOM is compared. What is more, the SOM learned with the RF proximity matrix has better classification accuracy in comparison to SOM learned with Euclidean distance. Presented approach enables better understanding of the RF and additionally improves accuracy of the SOM.

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An Adaptive Neighborhood Retrieval Visualizer

Olszewski

2020

Lecture Notes in Computer Science

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An Experimental Study on Asymmetric Self-Organizing Map

Cited by 9 publications

References 8 publications

Asymmetric $$k$$ k -Means Clustering of the Asymmetric Self-Organizing Map

Asymmetric $$k$$ k -Means Clustering of the Asymmetric Self-Organizing Map

Visualizing Random Forest with Self-Organising Map

An Adaptive Neighborhood Retrieval Visualizer

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