A review of feature subset selection on unsupervised learning

Kumar, R. Ravi; Reddy, M. Babu; Praveen, P.

doi:10.1109/aeeicb.2017.7972404

Cited by 27 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The responsibility of UL is to learn how to recognize patterns or acquire information from a dataset without predefined response or features. The utilization of such methods also enables the reduction of a dataset's dimensionality without the loss of relevant information [40]. Therefore, UL can discover essential information with minimal training data which comprises only a few elements.…”

Section: Three Groups In MLmentioning

confidence: 99%

Emerging Information Technologies for the Energy Management of Onboard Microgrids in Transportation Applications

Huang,

Xiao,

Gao

et al. 2023

Energies

View full text Add to dashboard Cite

The global objective of achieving net-zero emissions drives a significant electrified trend by replacing fuel-mechanical systems with onboard microgrid (OBMG) systems for transportation applications. Energy management strategies (EMS) for OBMG systems require complicated optimization algorithms and high computation capabilities, while traditional control techniques may not meet these requirements. Driven by the ability to achieve intelligent decision-making by exploring data, artificial intelligence (AI) and digital twins (DT) have gained much interest within the transportation sector. Currently, research on EMS for OBMGs primarily focuses on AI technology, while overlooking the DT. This article provides a comprehensive overview of both information technology, particularly elucidating the role of DT technology. The evaluation and analysis of those emerging information technologies are explicitly summarized. Moreover, this article explores potential challenges in the implementation of AI and DT technologies and subsequently offers insights into future trends.

show abstract

Section: Three Groups In MLmentioning

confidence: 99%

Emerging Information Technologies for the Energy Management of Onboard Microgrids in Transportation Applications

Huang,

Xiao,

Gao

et al. 2023

Energies

View full text Add to dashboard Cite

show abstract

“…The whole multifaceted nature of the proposed calculation is O(logn) and required (n/2 or n/2+1) to cluster all data focuses in one cluster though the past calculation is O(n²) and need (n-1) ventures to cluster all data focuses into one cluster [10,13]. In 2012, Shengrui Wang, Dan Wei, Qingshan Jiang, Yanjie Wei proposed a strategy is which assesses clustering practically related quality arrangements and by phylogenetic investigation [14]. In this paper, an introduction of a novel approach for DNA succession clustering, in view of another arrangement likeness measure DMk which is separated from DNA groupings in light of the position and sythesis of oligonucleotide design.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving Efficiency and Effectiveness of Hierarchical Clustering

Praveen¹,

Rama²

2018

ijarcs

Self Cite

View full text Add to dashboard Cite

Clustering techniques will formulate the edifice of the groups by divide the instances in whichever a bottom-up or top-down fashion. These methods are divided into Divisive hierarchical clustering and Agglomerative hierarchical clustering. The nested combining of objects and corollary levels at which groupings change will be represented by the corollary of these methods. The clustered items are achieved by wounding dendrogram at the desired likeness rank. Here the Single linkage method is inter dependent on correlation of two clusters that are nearest points in different clusters. Complete linkage method is reliant on the correlation of two clusters that are least similar points in the different clusters. Average linkage method is reliant on the average of pair wise closeness between the points in two clusters. For choosing which strategies are most appropriate for a given dataset, here we proposed a ensemble based system

show abstract

Big Data Clustering: Applying Conventional Data Mining Techniques in Big Data Environment

Praveen¹,

Babu

2019

Innovations in Computer Science and Engineering

View full text Add to dashboard Cite

A review of feature subset selection on unsupervised learning

Cited by 27 publications

References 6 publications

Emerging Information Technologies for the Energy Management of Onboard Microgrids in Transportation Applications

Emerging Information Technologies for the Energy Management of Onboard Microgrids in Transportation Applications

Improving Efficiency and Effectiveness of Hierarchical Clustering

Big Data Clustering: Applying Conventional Data Mining Techniques in Big Data Environment

Contact Info

Product

Resources

About