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

Zhu, Guangming; Zhu, Xingyin

doi:10.1109/aici.2010.276

Cited by 8 publications

(4 citation statements)

References 9 publications

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“…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.…”

Section: Literature Reviewmentioning

confidence: 97%

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

2023

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Due to the everchanging and evergrowing nature of programming technologies, the gap between the programming industry’s needs and the educational capabilities of both formal and informal educational environments has never been wider. However, the need to learn computer programming has never been greater, regardless of the motivation behind it. The number of programming concepts to be taught is increasing over time, while the amount of time available for education and training usually remains the same. The objective of this research was to analyze the source codes used in many educational systems to teach fundamental programming concepts to learners, regardless of their prior experience in programming. A total of 25 repositories containing 3882 Python modules were collected for the analysis. Through self-organization of the collected content, we obtained very compelling results about code structure, distribution, and differences. Based on those results, we concluded that Self-Organizing Maps are a powerful tool for both content and knowledge management, because they can highlight problems in the curriculum’s density as well as transparently indicate which programming concepts it has successfully observed and learned to recognize. Based on the level of transparency exhibited by Self-Organizing Maps, it is safe to say that they could be used in future research to enhance both human and machine learning of computer programming. By achieving this level of transparency, such an Artificial Intelligence system would be able to assist in overall computer programming education by communicating what should be taught, what needs to be learned, and what is known.

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Section: Literature Reviewmentioning

confidence: 97%

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

2023

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“…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.…”

Section: Gambar 2 Metodelogi Penelitianunclassified

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

Banyal

Surianti²

2020

JSI

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Metagenome merupakan mikroorganisme yang diambil secara langsung dari alam. Proses sequenc inggenom dari metagenome mengakibatkan bercampurnya berbagai organisme. Metode pengumpulan data yang digunakan adalah Observasi,menggunakan teknik terjun langsung ke lapangan pada Dinas Lingkungan Hidup Kota Jayapura. Penelitian ini menggunakan data fragmen metagenom dari 300 mikrob. Teknik pengambilan data fragmen metagenom yang digunakan adalah cluster sampling. Lokasi penelitian bertempat di Dinas Lingkungan Hidup Kota Jayapura. Tujuan penelitian ini adalah untk menganalisis efektifitas dan efisiensi metode Growing Self Organizing Map dalam pengelompokan mikrob yang berskala besar dengan panjang fragmen yang pendek berdasarkan frekuensi oligonukleotida. Untuk ekstraksi fitur, digunakan k-mer frequency dan spaced. Digunakan fragmen yang pendek karena pada penelitan terdahulu, panjang fragmen yang digunakan adalah fragmen yang panjang (≥ 8 kbp), sehingga pada penelitian ini hendak mengatasi kelemahan dari penggunaan fragmen pendek dalam pengelompokan fragmen metagenom. Hasil dari pengelompokan fragmen metagenom tersebut akan di uji efektifitas dan efisiensinya.

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“…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.…”

Section: Clustersmentioning

confidence: 99%

Patient similarity: methods and applications

Dai¹,

Zhu²,

Liu

2020

Preprint

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Patient similarity analysis is important in health care applications. It takes patient information such as their electronic medical records and genetic data as input and computes the pairwise similarity between patients. Procedures of typical a patient similarity study can be divided into several steps including data integration, similarity measurement, and neighborhood identification. And according to an analysis of patient similarity, doctors can easily find the most suitable treatments. There are many methods to analyze the similarity such as cluster analysis. And during machine learning become more and more popular, Using neural networks such as CNN is a new hot topic. This review summarizes representative methods used in each step and discusses applications of patient similarity networks especially in the context of precision medicine.

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

Cited by 8 publications

References 9 publications

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

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

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

Patient similarity: methods and applications

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