The Growing Self-organizing Map for Clustering Algorithms in Programming Codes

Abstract

The growing self-organizing map (GSOM) is a variation of the popular self-organizing map (SOM). It was developed to address the issue of identifying a suitable size of the SOM, which is usually concerned with vectorial items. To deal with algoritms implemented as programs, which are hardly represented by vectors, a new version of GSOM for clustering non-vectorial items (GSOM/NV) is proposed here. By syntax analysis, source codes of programs are converted into syntax trees, on a basis of which similarities between these codes are computed, so that the normal GSOM could be applied to clustering the algorithms that are implemented as the programs. An experiment shows that those whose implemented algorithms are the same, but coded differently each other, are gathered together on the visualization map generated by the proposed method.

Source Code Analysis in Programming Education: Evaluating Learning Content with Self-Organizing Maps

“…Additionally, SOMs have been used to group procedures with similar properties by identifying common features in software code [12,16]. Furthermore, a study demonstrated that the variation of SOMs can identify algorithms implemented as programs by converting source code into syntax trees and computing similarities between them [17]. In conclusion, the studies suggest that SOMs can effectively analyze and cluster programming code, making them a valuable tool in this domain.…”

Patient similarity: methods and applications

“…At the data integration stage, intermediate data integration technology such as non-negative matrix factorization methods used in studying disease-disease association and human chromatin interaction can be adapted to minimize information loss [ŽJL + 13, ŽZ15, LDVZK19].More similarity metrics can be introduced into detecting patient similarity, especially supervised and semi-supervised methods. At the neighborhood/cluster detection stage, many advanced technologies like growing SOM and semi-supervised clustering can be utilized [KMJRW14,ZZ10]. One important field to which patient similarity network could make a contribution to precision medicine which can be considered a special type of personalized data science.…”

CLUSTERING FRAGMEN METAGENOM MENGGUNAKAN METODE GROWING SELF ORGANIZING MAP (GSOM) ( Studi Kasus Dinas Lingkungan Hidup Kota Jayapura)

“…Ekstraksi ciri akan menampilkan pola kemunculan k pada suatu waktu dalam suatu sekuens. Pra proses data dilakukan Untuk mencegah adanya hasil implementasi yang bias, maka pengelompokan fragmen metagenom didahului dengan [16]. normalisasi data hasil ekstraksi fitur.…”