Review of single clustering methods

Mahfuz, Nurshazwani Muhamad; Yusoff, Marina; Ahmad, Zakiah

doi:10.11591/ijai.v8.i3.pp221-227

Cited by 6 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To this end hclust function of R package with single algorithm andnhattan distance is used. [74] Since many identified clusters are characterized by a size too small, these certainly cannot be defined as membrane channels, but rather they are due to noise. For this reason, we only select objects with a pixel count greater than 150 and lower than 2000 (in order not to include regions that represent image artifacts).…”

Section: Generation Of Human Induced Pluripotent Stem Cells (Hipscs)-...mentioning

confidence: 99%

Direct Visualization and Identification of Membrane Voltage‐Gated Sodium Channels from Human iPSC‐Derived Neurons by Multiple Imaging and Light Enhanced Spectroscopy

et al. 2022

View full text Add to dashboard Cite

In this study, transmission electron microscopy atomic force microscopy, and surface enhanced Raman spectroscopy are combined through a direct imaging approach, to gather structural and chemical information of complex molecular systems such as ion channels in their original plasma membrane. Customized microfabricated sample holder allows to characterize Nav channels embedded in the original plasma membrane extracted from neuronal cells that are derived from healthy human induced pluripotent stem cells. The identification of the channels is accomplished by using two different approaches, one of them widely used in cryo‐EM (the particle analysis method) and the other based on a novel Zernike Polynomial expansion of the images bitmap. This approach allows to carry out a whole series of investigations, one complementary to the other, on the same sample, preserving its state as close as possible to the original membrane configuration.

show abstract

Section: Generation Of Human Induced Pluripotent Stem Cells (Hipscs)-...mentioning

confidence: 99%

Direct Visualization and Identification of Membrane Voltage‐Gated Sodium Channels from Human iPSC‐Derived Neurons by Multiple Imaging and Light Enhanced Spectroscopy

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Clustering is an unsupervised data mining technique [26][27][28] focusing on grouping together homogenous objects. The purpose of clustering is to extract groups/clusters and comparable data objects [29], [37].…”

Section: Trace Clusteringmentioning

confidence: 99%

Process Discovery Enhancement with Trace Clustering and Profiling

Faizan¹,

Zuhairi²,

Ismail³

2021

AETiC

View full text Add to dashboard Cite

The potential in process mining is progressively growing due to the increasing amount of event-data. Process mining strategies use event-logs to automatically classify process models, recommend improvements, predict processing times, check conformance, and recognize anomalies/deviations and bottlenecks. However, proper handling of event-logs while evaluating and using them as input is crucial to any process mining technique. When process mining techniques are applied to flexible systems with a large number of decisions to take at runtime, the outcome is often unstructured or semi-structured process models that are hard to comprehend. Existing approaches are good at discovering and visualizing structured processes but often struggle with less structured ones. Surprisingly, process mining is most useful in domains where flexibility is desired. A good illustration is the "patient treatment" process in a hospital, where the ability to deviate from dealing with changing conditions is crucial. It is useful to have insights into actual operations. However, there is a significant amount of diversity, which contributes to complicated, difficult-to-understand models. Trace clustering is a method for decreasing the complexity of process models in this context while also increasing their comprehensibility and accuracy. This paper discusses process mining, event-logs, and presenting a clustering approach to pre-process event-logs, i.e., a homogeneous subset of the event-log is created. A process model is generated for each subset. These homogeneous subsets are then evaluated independently from each other, which significantly improving the quality of mining results in flexible environments. The presented approach improves the fitness and precision of a discovered model while reducing its complexity, resulting in well-structured and easily understandable process discovery results.

show abstract

“…The researcher deals with the communication between a large topology wireless sensors and MANETs networks. Analyzing the effect of mobile networks on the critical transition range, as the handover for asymptotic connectivity, in a multi-level cluster networks in comparison with stationary networks [25]. By integrating the k-hop criterion, all messages sent from a cluster sending station could be transmitted to the cluster master in the k-hops, and thus the transmission delay is limited for the finite k. First, the article defines the critical transition range for the communication of mobile k-hop clusters, these move with a stochastic mobility model.…”

Section: Conception and Integration Of The Mssp In Manet Olsr Clustermentioning

confidence: 99%

QoS routing in cluster OLSR by using the artificial intelligence model MSSP in the big data environnment

Oubaha

Lakki

Ouacha

2021

IJ-AI

View full text Add to dashboard Cite

The most complex problems, in data science and more specifically in artificial intelligence, can be modeled as cases of the maximum stable set problem (MSSP). this article describes a new approach to solve the MSSP problem by proposing the continuous hopfield network (CHN) to build optimized link state protocol routing (OLSR) protocol cluster. our approach consists in proposing in two stages: the first acts at the level of the choice of the OLSR master cluster in order to quickly make a local minimum using the CHN, by modeling the MSSP problem. As for the second step, the objective is the improvement of the precision making a solution of efficient at the first rank of neighborhood as a linear constraint, and at the end, to find the resolution of the model using the CHN. We will show that this model determines a good solution of the MSSP problem. To test the theoretical results, we propose a comparison with a classic OLSR.

show abstract

Review of single clustering methods

Cited by 6 publications

References 0 publications

Direct Visualization and Identification of Membrane Voltage‐Gated Sodium Channels from Human iPSC‐Derived Neurons by Multiple Imaging and Light Enhanced Spectroscopy

Direct Visualization and Identification of Membrane Voltage‐Gated Sodium Channels from Human iPSC‐Derived Neurons by Multiple Imaging and Light Enhanced Spectroscopy

Process Discovery Enhancement with Trace Clustering and Profiling

QoS routing in cluster OLSR by using the artificial intelligence model MSSP in the big data environnment

Contact Info

Product

Resources

About