Review of dimensionality reduction techniques using clustering algorithm in reconstruction of gene regulatory networks

Pindah, Winddy; Nordin, Sharifalillah; Seman, Ali; Said, Mohamed Saifulaman Mohamed

doi:10.1109/i4ct.2015.7219560

Cited by 10 publications

(6 citation statements)

References 25 publications

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“…Clustering methods have been highlighted in many research and applied in many domains [9][10][11][12][13]. In clustering the idea is not to predict the target class as like classification, it is more ever trying to group the similar kind of things by considering the most satisfied conditions all the items in the same group should be similar and no two different group items should not be similar [14].…”

Section: Clustering Methodsmentioning

confidence: 99%

Evaluation of Clustering Methods for Student Learning Style Based Neuro Linguistic Programming

Yusoff¹,

Fathi

2018

IJET

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Students' performance is a key point to get a better first impression during a job interview with an employer. However, there are several factors, which affect students' performances during their study. One of them is their learning style, which is under Neurolinguistic Programming (NLP) approach. Learning style is divided into a few behavioral categories, Visual, Auditory and Kinesthetics (VAK). This paper addresses the evaluation of clustering methods for the identification of learning style based on system preferences. It starts with the distribution of questionnaires to acquire the information on the VAK for each student. About 167 respondents in the Faculty of Computer and Mathematical Science are collected. It is then pre-processed to prepare the data for clustering method evaluations. Three clustering methods; Simple K-Mean, Hierarchical and Density-Based Spatial Clustering of Applications with Noise are evaluated. The findings show that Simple K-Mean offers the most accurate prediction. Upon completion, by using the dataset, Simple K-Means technique estimated four clusters that yield the highest accuracy of 74.85 % compared to Hierarchical Clustering, which estimated four clusters and Density-Based Spatial Clustering of Applications with Noise which estimated three clusters with 52.69% and 61.68 % respectively. The clustering method demonstrates the capability of categorizing the learning style of students based on three categories; visual, auditory and kinesthetic. This outcome would be beneficial to lecturers or teachers in university and school with an automatically clustering the students' learning style and would assist them in teaching and learning, respectively.

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Section: Clustering Methodsmentioning

confidence: 99%

Evaluation of Clustering Methods for Student Learning Style Based Neuro Linguistic Programming

Yusoff¹,

Fathi

2018

IJET

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“…These techniques are often used as data quantisation and dimensionality reduction techniques [54][55][56] and they have been vastly applied in robotics [57][58][59].…”

Section: Unsupervised Learningmentioning

confidence: 99%

Machine Autonomy: Definition, Approaches, Challenges and Research Gaps

Ezenkwu

Starkey

2019

Advances in Intelligent Systems and Computing

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The processes that constitute the designs and implementations of AI systems such as self-driving cars, factory robots and so on have been mostly hand-engineered in the sense that the designers aim at giving the robots adequate knowledge of its world. This approach is not always efficient especially when the agent's environment is unknown or too complex to be represented algorithmically. A truly autonomous agent can develop skills to enable it to succeed in such environments without giving it the ontological knowledge of the environment a priori. This paper seeks to review different notions of machine autonomy and presents a definition of autonomy and its attributes. The attributes of autonomy as presented in this paper are categorised into low-level and high-level attributes. The low-level attributes are the basic attributes that serve as the separating line between autonomous and other automated systems while the high-level attributes can serve as a taxonomic framework for ranking the degrees of autonomy of any system that has passed the low-level autonomy. The paper reviews some AI techniques as well as popular AI projects that focus on autonomous agent designs in order to identify the challenges of achieving a true autonomous system and suggest possible research directions.

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“…Therefore, extracting and selecting the most discriminative information from observed data is a crucial step in data-based modeling, which is known as dimensionality reduction. As a generally accepted rule, dimensionality reduction can be classified into two categories: feature selection and feature extraction [34], [37]. Feature selection refers to selecting a portion of the original dimensions that are most important for the task, while feature extraction refers to extracting a new representation set from the original dimension space [38]- [40].…”

Section: Introductionmentioning

confidence: 99%

Robust and Efficient Classification for Underground Metal Target Using Dimensionality Reduction and Machine Learning

Wan

Wang

et al. 2021

IEEE Access

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Review of dimensionality reduction techniques using clustering algorithm in reconstruction of gene regulatory networks

Cited by 10 publications

References 25 publications

Evaluation of Clustering Methods for Student Learning Style Based Neuro Linguistic Programming

Evaluation of Clustering Methods for Student Learning Style Based Neuro Linguistic Programming

Machine Autonomy: Definition, Approaches, Challenges and Research Gaps

Robust and Efficient Classification for Underground Metal Target Using Dimensionality Reduction and Machine Learning

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