Neural Network with K-Means Clustering via PCA for Gene Expression Profile Analysis

Chen, Thomas C.; Sanga, Sandeep; Chou, Tina Y.; Cristini, Vittorio; Edgerton, Mary E.

doi:10.1109/csie.2009.945

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Setiap kata yang berbeda dalam dokumen teks akan menjadi atribut baru setelah dikonversi menjadi data numerik sehingga data memiliki banyak dimensi [4]. Pada data berdimensi tinggi dapat muncul data yang kompleks, yaitu data dengan noise, anomali (outlier), kehilangan makna (missing values), dan diskontinuitas antar data [5,6]…”

Section: Pendahuluanunclassified

Pengaruh Reduksi Dimensi Terhadap Metode Pengklasteran Berbasis Centroid Dan Metode Pengklasteran Berbasis Density Dalam Pengklasteran Dokumen Teks

Jambak

Efendi

2021

IJUBI

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Density-based clustering is usually more effective when processing data of different densities. This method is pioneered by the Density-based Applied Noise Spatial Clustering (DBSCAN) algorithm. There is a significant difference in behavior between k-Means and DBSCAN, which is processing data that contains noise. To this end, this research studies the impact of dimensionality reduction on high-dimensional data on the clustering results of the k-Means algorithm represented by the centroid method and the clustering results of the DBSCAN algorithm represented by the density method. Although the quality of the clustering results on k-Means has been improved after the numerical reduction by Singular Value Decomposition (SVD), from the initial average distance of 1.04136 to 0.003, the statistical change is not significant or considered to be the same. Therefore, it can be concluded statistically that SVD has no effect on the quality of k-Means clustering results. On the other hand, in DBSCAN, the effect of SVD dimensionality reduction is very significant. It can change the quality of the clustering results from the initial average intracluster distance of 76.13480 to 13.71130 or improve the quality by 555.27%. The significant impact of SVD on SVD + k-Means optimization and SVD + DBSCAN optimization cluster calculation time changes is also shown. SVD optimization can accelerate k-Means calculation time from 3.68182 seconds to 2,09091 seconds or 1.76 times. At the same time, SVD optimization accelerates the DBSCAN calculation time from 19.40000 seconds to 0.97500 seconds or 19.89 times. Kata Kunci: hingga 5 kata kunci, ditulis miring, dengan pemisah koma. AbstrakPengklasteran berbasis kepadatan biasanya lebih efisien bekerja pada data dengan kepadatan yang berbeda, dimana metoda ini dipelopori oleh algoritma Density-Based Spatial Clustering of Application with Noise (DBSCAN). Terdapat perbedaan perilaku yang signifikan antara k-Means dan DBSCAN, yaitu perlakuan terdapat data yang mengandung noise. Untuk itu maka penelitian ini mempelajari pengaruhi reduksi dimensi pada data berdimensi tinggi terhadap hasil pengklasteran oleh algoritma k-Means yang mewakili metode centroid dengan hasil pengklasteran oleh algoritma DBSCAN yang mewakili metode density. Sekalipun secara numerikal terjadi peningkatan kualitas hasil klaster pada k-Means setelah direduksi oleh Singular Value Decomposition (SVD), yaitu dari rata-rata jarak awal 1.04136 menjadi 0.003, namun secara statistik perubahan tersebut tidak signifikan atau dianggap sama. Sehingga secara statistik dapat disimpulkan bahwa tidak ada pengaruh oleh SVD terhadap kualitas hasil klaster oleh k-Means. Sebaliknya pada DBSCAN pengaruh reduksi dimensi oleh SVD sangat signifikan dimana mampu mengubah

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Section: Pendahuluanunclassified

Pengaruh Reduksi Dimensi Terhadap Metode Pengklasteran Berbasis Centroid Dan Metode Pengklasteran Berbasis Density Dalam Pengklasteran Dokumen Teks

Jambak

Efendi

2021

IJUBI

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“…Dimensionality reduction is one potential method to reduce the number of features of the data [161]. For instance, in [162], Chen et al used neural network with k-means clustering via principal component analysis (PCA) to reduce the complexity and the number of dimensions of gene expression data to extract disease-related information from gene expression profiles. Knowledge discovery in databases (KDD) is also used in different CPS scenarios to find hidden patterns and unknown correlations in data so that useful information can be converted into knowledge [163].…”

Section: A Data Miningmentioning

confidence: 99%

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

et al. 2018

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The world is witnessing an unprecedented growth of cyber-physical systems (CPS), which are foreseen to revolutionize our world via creating new services and applications in a variety of sectors such as environmental monitoring, mobilehealth systems, intelligent transportation systems and so on. The information and communication technology (ICT) sector is experiencing a significant growth in data traffic, driven by the widespread usage of smartphones, tablets and video streaming, along with the significant growth of sensors deployments that are anticipated in the near future. It is expected to outstandingly increase the growth rate of raw sensed data. In this paper, we present the CPS taxonomy via providing a broad overview of data collection, storage, access, processing and analysis. Compared with other survey papers, this is the first panoramic survey on big data for CPS, where our objective is to provide a panoramic summary of different CPS aspects. Furthermore, CPS require cybersecurity to protect them against malicious attacks and unauthorized intrusion, which become a challenge with the enormous amount of data that is continuously being generated in the network. Thus, we also provide an overview of the different security solutions proposed for CPS big data storage, access and analytics. We also discuss big data meeting green challenges in the contexts of CPS.

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Dimension Reduction with Extraction Methods (Principal Component Analysis - Self Organizing Map - Isometric Mapping) in Indonesian Language Text Documents Clustering

Jambak

Febrianto

et al. 2020

Hybrid Intelligent Systems

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Neural Network with K-Means Clustering via PCA for Gene Expression Profile Analysis

Cited by 4 publications

References 5 publications

Pengaruh Reduksi Dimensi Terhadap Metode Pengklasteran Berbasis Centroid Dan Metode Pengklasteran Berbasis Density Dalam Pengklasteran Dokumen Teks

Pengaruh Reduksi Dimensi Terhadap Metode Pengklasteran Berbasis Centroid Dan Metode Pengklasteran Berbasis Density Dalam Pengklasteran Dokumen Teks

Big Data Meet Cyber-Physical Systems: A Panoramic Survey

Dimension Reduction with Extraction Methods (Principal Component Analysis - Self Organizing Map - Isometric Mapping) in Indonesian Language Text Documents Clustering

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