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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Multi-objective differential evolution for automatic clustering with application to micro-array data analysis.

Kaushik Suresh1, Debarati Kundu, Sayan Ghosh

  • 1Dept. of Electronics and Telecommunication Engg, Jadavpur University, Kolkata, India; E-Mails: kaushik_s1988@yahoo.com ; kundu.debarati@gmail.com ; sayan88tito@gmail.com ; swagatamdas19@yahoo.co.in.

Sensors (Basel, Switzerland)
|March 14, 2012
PubMed
Summary
This summary is machine-generated.

Differential Evolution (DE) shows promise for multi-objective (MO) fuzzy clustering. This optimization approach effectively handles conflicting validity indices, offering a flexible set of solutions for complex datasets.

Keywords:
differential evolutionfuzzy clusteringmicro-array data clusteringmulti-objective optimization

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Area of Science:

  • Computational intelligence
  • Data mining
  • Optimization algorithms

Background:

  • Fuzzy clustering is essential for data analysis, but optimizing multiple conflicting objectives remains challenging.
  • Existing multi-objective clustering algorithms have limitations in handling complex datasets and variable cluster numbers.

Purpose of the Study:

  • To apply and evaluate two multi-objective variants of the Differential Evolution (DE) algorithm for automatic fuzzy clustering.
  • To compare the performance of DE-based methods against established multi-objective clustering algorithms like NSGA II and MOCK.

Main Methods:

  • Utilized a real-coded representation for DE to accommodate a variable number of cluster centers.
  • Simultaneously optimized two conflicting fuzzy validity indices within a multi-objective optimization framework.
  • Compared DE variants with Non Dominated Sorting Genetic Algorithm II (NSGA II) and Multi-Objective Clustering with an unknown number of Clusters K (MOCK).

Main Results:

  • DE-based multi-objective fuzzy clustering demonstrated strong performance across six artificial and four real-life datasets.
  • The Pareto optimal sets generated by DE provided a range of non-dominated solutions for user selection.
  • DE variants showed competitive or superior results compared to NSGA II and MOCK in experimental evaluations.

Conclusions:

  • Differential Evolution (DE) is a highly promising algorithm for developing effective multi-objective fuzzy clustering schemes.
  • The flexibility in handling variable cluster numbers and optimizing conflicting indices makes DE suitable for diverse clustering tasks.
  • Further research into DE for advanced data clustering applications is warranted.