High Dimensional Data Clustering using Self-Organized Map

Febrita, Ruth Ema; Mahmudy, Wayan Firdaus; Wibawa, Aji Prasetya

doi:10.17977/um018v2i12019p31-40

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…Nilai silhouette coeficient terletak dalam range [-1,1], dimana hasil cluster efektif apabila nilai silhouette semakin mendekati 1, dan sangat tidak efektif apabila nilai silhouette mendekati -1. Cluster disebut overlap apabila nilai silhouette = 0 (Febrita et al, 2019).…”

Section: Evaluasiunclassified

Penentuan Kekerabatan Hewan Berdasarkan Struktur Protein IGF2 Menggunakan Metode K-Means dan N-Gram

Febrita

Amaniyah²

2022

Jurnal Penelitian Teknik Informatika, Manajemen Informatika dan

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Dalam ilmu Biologi, terdapat berbagai cara untuk menentukan kedekatan antar dua individu, antara lain dengan mengamati kesamaan morfologi fisik kemudian membuat dendogram dan pembuatan pohon filogeni untuk menelusur kekerabatan berdasarkan sejarah evolusi suatu makhluk hidup. Akan tetapi pendekatan ini sangat sulit untuk dilakukan apabila hewan yang akan ditentukan kekerabatannya tidak berada dalam kondisi yang hidup, sehingga sangat sulit untuk mengamati ciri-ciri fisik yang ada. Penelitian ini bertujuan untuk memberikan pendekatan yang berbeda dalam menentukan kekerabatan hewan dengan menggunakan algoritma clustering untuk mengelompokkan struktur protein IGF2. Kekerabatan dilakukan dengan menggunakan metode clustering K-Means. Untuk memudahkan dalam melakukan pengelompokkan struktur protein yang memiliki panjang sekuens yang beragam, maka teknik n-gram digunakan untuk memecah string menjadi beberapa subsekuens dengan panjang yang sama. Pengelompokkan dengan metode K-Means telah dilakukan dan mendapatkan hasil terbaik pada jumlah cluster sebanyak tujuh cluster, dengan silhouette coeficient rata-rata sebesar 0.331, indeks puritysebesar 0.735, dan precisionsebesar 0.823 yang mengindikasikan proses clustering cukup efektif.

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Section: Evaluasiunclassified

Penentuan Kekerabatan Hewan Berdasarkan Struktur Protein IGF2 Menggunakan Metode K-Means dan N-Gram

Febrita

Amaniyah²

2022

Jurnal Penelitian Teknik Informatika, Manajemen Informatika dan

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“…In turn, a SOM-inspired algorithm for partition clustering is proposed in [33]. The SOM-based or inspired data clustering finds applications ranging from dengue expression data analysis [33] to house data grouping [34]. It is worth mentioning here also a collection of earlier published two-level approaches to data clustering using SOMs [35][36][37][38] (see also [39,40] for brief reviews).…”

Section: Related Recent Workmentioning

confidence: 99%

Evolution of SOMs’ Structure and Learning Algorithm: From Visualization of High-Dimensional Data to Clustering of Complex Data

Gorzałczany

Rudziński

2020

Algorithms

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In this paper, we briefly present several modifications and generalizations of the concept of self-organizing neural networks—usually referred to as self-organizing maps (SOMs)—to illustrate their advantages in applications that range from high-dimensional data visualization to complex data clustering. Starting from conventional SOMs, Growing SOMs (GSOMs), Growing Grid Networks (GGNs), Incremental Grid Growing (IGG) approach, Growing Neural Gas (GNG) method as well as our two original solutions, i.e., Generalized SOMs with 1-Dimensional Neighborhood (GeSOMs with 1DN also referred to as Dynamic SOMs (DSOMs)) and Generalized SOMs with Tree-Like Structures (GeSOMs with T-LSs) are discussed. They are characterized in terms of (i) the modification mechanisms used, (ii) the range of network modifications introduced, (iii) the structure regularity, and (iv) the data-visualization/data-clustering effectiveness. The performance of particular solutions is illustrated and compared by means of selected data sets. We also show that the proposed original solutions, i.e., GeSOMs with 1DN (DSOMs) and GeSOMS with T-LSs outperform alternative approaches in various complex clustering tasks by providing up to 20 % increase in the clustering accuracy. The contribution of this work is threefold. First, algorithm-oriented original computer-implementations of particular SOM’s generalizations are developed. Second, their detailed simulation results are presented and discussed. Third, the advantages of our earlier-mentioned original solutions are demonstrated.

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The Implementation of Machine Learning for Optimizing Network-Based Intrusion Detection in the Snort Application

Febrita,

Hakim,

Utomo

2023

2023 6th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)

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High Dimensional Data Clustering using Self-Organized Map

Cited by 3 publications

References 20 publications

Penentuan Kekerabatan Hewan Berdasarkan Struktur Protein IGF2 Menggunakan Metode K-Means dan N-Gram

Penentuan Kekerabatan Hewan Berdasarkan Struktur Protein IGF2 Menggunakan Metode K-Means dan N-Gram

Evolution of SOMs’ Structure and Learning Algorithm: From Visualization of High-Dimensional Data to Clustering of Complex Data

The Implementation of Machine Learning for Optimizing Network-Based Intrusion Detection in the Snort Application

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