Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Methods of Medium Optimization01:28

Methods of Medium Optimization

53
Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
53
Survival Tree01:19

Survival Tree

497
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
497
Optimization Problems01:26

Optimization Problems

185
Optimization problems often involve identifying maximum or minimum values under specific constraints. A well-known example is determining the longest horizontal pipe that can be moved around a right-angled corner, where a 3-meter-wide hallway meets a 2-meter-wide hallway. This scenario, common in architectural design and industrial transport, can be understood conceptually through geometric and trigonometric reasoning.To visualize the problem, consider the pipe as a straight line that touches...
185
Aggregates Classification01:29

Aggregates Classification

1.2K
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
1.2K
Classification of Systems-II01:31

Classification of Systems-II

565
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
565
Classification of Systems-I01:26

Classification of Systems-I

673
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
673

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A hybrid metaheuristic-deep learning technique for the pan-classification of cancer based on DNA methylation.

BMC bioinformaticsยท2022
Same author

Novel trajectory clustering method based on distance dependent Chinese restaurant process.

PeerJ. Computer scienceยท2021
Same author

Automatic Region-Based Brain Classification of MRI-T1 Data.

PloS oneยท2016
Same author

Magnetic resonance image tissue classification using an automatic method.

Diagnostic pathologyยท2014
Same author

An analytical approach to evaluate the performance of graphene and carbon nanotubes for NH3 gas sensor applications.

Beilstein journal of nanotechnologyยท2014
Same author

Development of solution-gated graphene transistor model for biosensors.

Nanoscale research lettersยท2014

Related Experiment Video

Updated: Apr 4, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K

An Efficient Optimization Method for Solving Unsupervised Data Classification Problems.

Parvaneh Shabanzadeh1, Rubiyah Yusof1

  • 1Centre for Artificial Intelligence and Robotics, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia ; Malaysia-Japan International Institute of Technology (MJIIT), Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia.

Computational and Mathematical Methods in Medicine
|September 4, 2015
PubMed
Summary

A new approach adapts the Biogeography-Based Optimization (BBO) algorithm for unsupervised data classification. This evolutionary optimization method shows efficiency in grouping similar data points across various applications, including medical fields.

More Related Videos

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.4K

Related Experiment Videos

Last Updated: Apr 4, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.4K

Area of Science:

  • Data Mining and Machine Learning
  • Computational Biology and Medical Informatics

Background:

  • Unsupervised data classification, or clustering, is crucial for identifying homogeneous groups in data mining.
  • Existing algorithms have limitations, necessitating research into novel and effective unsupervised classification approaches.
  • Applications span numerous medical disciplines and real-world scenarios.

Purpose of the Study:

  • To adapt the Biogeography-Based Optimization (BBO) algorithm for data clustering.
  • To evaluate the performance of the modified BBO algorithm for unsupervised data classification.

Main Methods:

  • Modified the main operators of the Biogeography-Based Optimization (BBO) algorithm, inspired by species distribution.
  • Applied the adapted BBO algorithm to six medical and real-life datasets.
  • Compared the proposed algorithm against eight established unsupervised data classification algorithms.

Main Results:

  • The adapted Biogeography-Based Optimization (BBO) algorithm demonstrated efficiency in unsupervised data classification.
  • Numerical results confirmed the algorithm's effectiveness on diverse datasets.

Conclusions:

  • The modified Biogeography-Based Optimization (BBO) algorithm presents a viable and efficient evolutionary approach for unsupervised data classification.
  • This method holds promise for applications requiring robust data clustering, particularly in medical fields.