Journal Article10.3233/FI-2009-208
Data Clustering Using Multi-objective Differential Evolution Algorithms
TL;DR: Experimental results reported for six artificial and four real life datasets of varying range of complexities indicates that DE can serve as a promising algorithm for devising MO clustering techniques.
read more
Abstract: The article considers the task of fuzzy clustering in a multi-objective optimization (MO) framework It compares the relative performance of four recently developedmulti-objective variants of Differential Evolution (DE) on over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications A real-coded representation for the candidates is used for DE A comparative study of four DE variants with two most well-known MO clustering techniques, namely the NSGA II (Non Dominated Sorting GA) and MOCK (Multi- Objective Clustering with an unknown number of clusters K) is also undertaken Experimental results reported for six artificial and four real life datasets (including a microarray dataset of budding yeast) of varying range of complexities indicates that DE can serve as a promising algorithm for devising MO clustering techniques
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects
Ezugwu E. Absalom,Abiodun Motunrayo Ikotun,Olaide Nathaniel Oyelade,Laith Abualigah,Jeffrey O. Agushaka,Christopher Ifeanyi Eke,Andronicus Ayobami Akinyelu +6 more
TL;DR: Clustering is an essential tool in data mining research and applications as discussed by the authors and it is the subject of active research in many fields of study, such as computer science, data science, statistics, pattern recognition, artificial intelligence, and machine learning.
573
A Survey of Multiobjective Evolutionary Clustering
TL;DR: A comprehensive and critical survey of the multitude of multiobjective evolutionary clustering techniques existing in the literature, classified according to the encoding strategies adopted, objective functions, evolutionary operators, strategy for maintaining nondominated solutions, and the method of selection of the final solution.
166
Ant Colony Optimization based clustering methodology
Tülin İnkaya,Sinan Kayaligil,Nur Evin Özdemirel +2 more
- 01 Mar 2015
TL;DR: A novel ACO based methodology (ACO-C) is proposed for spatial clustering that works in data sets with no a priori information, has a multi-objective framework, and yields a set of non-dominated solutions.
103
Evolutionary Multiobjective Clustering and Its Applications to Patient Stratification
Xiangtao Li,Ka-Chun Wong +1 more
TL;DR: A novel multiobjective framework called multiobjectives clustering algorithm by fast search and find of density peaks is proposed to address limitations altogether and can achieve better or competitive solutions than the others.
78
A two-stage solution method based on NSGA-II for Green Multi-Objective integrated process planning and scheduling in a battery packaging machinery workshop
TL;DR: The experimental results demonstrate the proposed optimization method can effectively solve GMOIPPS problem and show that the proposed method can handle real-world cases effectively.
70
References
A fast and elitist multiobjective genetic algorithm: NSGA-II
TL;DR: This paper suggests a non-dominated sorting-based MOEA, called NSGA-II (Non-dominated Sorting Genetic Algorithm II), which alleviates all of the above three difficulties, and modify the definition of dominance in order to solve constrained multi-objective problems efficiently.
Gene Ontology: tool for the unification of biology
M Ashburner,Catherine A. Ball,Judith A. Blake,David Botstein,Heather Butler,J. M. Cherry,Allan Peter Davis,Kara Dolinski,Selina S. Dwight,J.T. Eppig,Midori A. Harris,David P. Hill,Laurie Issel-Tarver,Andrew Kasarskis,Suzanna E. Lewis,John C. Matese,Joel E. Richardson,M. Ringwald,Gerald M. Rubin,Gavin Sherlock +19 more
TL;DR: The goal of the Gene Ontology Consortium is to produce a dynamic, controlled vocabulary that can be applied to all eukaryotes even as knowledge of gene and protein roles in cells is accumulating and changing.
Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
TL;DR: A new graphical display is proposed for partitioning techniques, where each cluster is represented by a so-called silhouette, which is based on the comparison of its tightness and separation, and provides an evaluation of clustering validity.
19K
Cluster analysis and display of genome-wide expression patterns
TL;DR: A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression, finding in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function.
•Book
Multi-Objective Optimization Using Evolutionary Algorithms
Kalyanmoy Deb,Deb Kalyanmoy +1 more
- 01 Jan 2001
TL;DR: This text provides an excellent introduction to the use of evolutionary algorithms in multi-objective optimization, allowing use as a graduate course text or for self-study.