Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
Journal of Global Optimization
Mining gene expression datasets using density-based clustering
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Evolutionary Algorithms for Clustering Gene-Expression Data
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Clustering of time-course gene expression data using functional data analysis
Computational Biology and Chemistry
Bioinformatics
Clustered alignments of gene-expression time series data
Bioinformatics
Hybrid differential evolution based on fuzzy C-means clustering
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
A survey of evolutionary algorithms for clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Clustering of unevenly sampled gene expression time-series data
Fuzzy Sets and Systems
Enhancing principal direction divisive clustering
Pattern Recognition
Improved differential evolution for microarray analysis
International Journal of Data Mining and Bioinformatics
Differential Evolution: A Survey of the State-of-the-Art
IEEE Transactions on Evolutionary Computation
Enhancing Differential Evolution Utilizing Proximity-Based Mutation Operators
IEEE Transactions on Evolutionary Computation
A weighted sum validity function for clustering with a hybrid niching genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
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The increasing flow of short time series microarray experiments for the study of dynamic cellular processes poses the need for efficient clustering tools. These tools must deal with three primary issues: first, to consider the multi-functionality of genes; second, to evaluate the similarity of the relative change of amplitude in the time domain rather than the absolute values; third, to cope with the constraints of conventional clustering algorithms such as the assignment of the appropriate cluster number. To address these, we propose OLYMPUS, a novel unsupervised clustering algorithm that integrates Differential Evolution (DE) method into Fuzzy Short Time Series (FSTS) algorithm with the scope to utilize efficiently the information of population of the first and enhance the performance of the latter. Our hybrid approach provides sets of genes that enable the deciphering of distinct phases in dynamic cellular processes. We proved the efficiency of OLYMPUS on synthetic as well as on experimental data. The discriminative power of OLYMPUS provided clusters, which refined the so far perspective of the dynamics of host response mechanisms to Influenza A (H1N1). Our kinetic model sets a timeline for several pathways and cell populations, implicated to participate in host response; yet no timeline was assigned to them (e.g. cell cycle, homeostasis). Regarding the activity of B cells, our approach revealed that some antibody-related mechanisms remain activated until day 60 post infection. The Matlab codes for implementing OLYMPUS, as well as example datasets, are freely accessible via the Web (http://biosignal.med.upatras.gr/wordpress/biosignal/).