An Integrative Clustering Approach Combining Particle Swarm Optimization and Formal Concept Analysis

Hristoskova, Anna; Boeva, Veselka; Tsiporkova, Elena

doi:10.1007/978-3-642-32395-9_7

Cited by 2 publications

(2 citation statements)

References 21 publications

(30 reference statements)

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“…In [29] the concept lattice is also used in an interactive way for concept selection and in a certain sense for providing plausible explanations. Other attempts on hybridization can be found in [33,54].…”

Section: An Application In the Mining Of Metabolomic Datamentioning

confidence: 99%

Elements About Exploratory, Knowledge-Based, Hybrid, and Explainable Knowledge Discovery

Couceiro

Napoli

2019

Formal Concept Analysis

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Knowledge Discovery in Databases (KDD) and especially pattern mining can be interpreted along several dimensions, namely data, knowledge, problem-solving and interactivity. These dimensions are not disconnected and have a direct impact on the quality, applicability, and efficiency of KDD. Accordingly, we discuss some objectives of KDD based on these dimensions, namely exploration, knowledge orientation, hybridization, and explanation. The data space and the pattern space can be explored in several ways, depending on specific evaluation functions and heuristics, possibly related to domain knowledge. Furthermore, numerical data are complex and supervised numerical machine learning methods are usually the best candidates for efficiently mining such data. However, the work and output of numerical methods are most of the time hard to understand, while symbolic methods are usually more intelligible. This calls for hybridization, combining numerical and symbolic mining methods to improve the applicability and interpretability of KDD. Moreover, suitable explanations about the operating models and possible subsequent decisions should complete KDD, and this is far from being the case at the moment. For illustrating these dimensions and objectives, we analyze a concrete case about the mining of biological data, where we characterize these dimensions and their connections. We also discuss dimensions and objectives in the framework of Formal Concept Analysis and we draw some perspectives for future research.

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Section: An Application In the Mining Of Metabolomic Datamentioning

confidence: 99%

Elements About Exploratory, Knowledge-Based, Hybrid, and Explainable Knowledge Discovery

Couceiro

Napoli

2019

Formal Concept Analysis

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“…In order to validate the proposed FCA-enhanced approach two consensus clustering algorithms have been developed, Integrative [ 13 ] and PSO-based [ 14 ], and used in the validation process. Note that, a preliminary FCA-enhanced version of the PSO-based algorithm was initially considered in [ 15 ].…”

Section: Related Workmentioning

confidence: 99%

A formal concept analysis approach to consensus clustering of multi-experiment expression data

2014

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BackgroundPresently, with the increasing number and complexity of available gene expression datasets, the combination of data from multiple microarray studies addressing a similar biological question is gaining importance. The analysis and integration of multiple datasets are expected to yield more reliable and robust results since they are based on a larger number of samples and the effects of the individual study-specific biases are diminished. This is supported by recent studies suggesting that important biological signals are often preserved or enhanced by multiple experiments. An approach to combining data from different experiments is the aggregation of their clusterings into a consensus or representative clustering solution which increases the confidence in the common features of all the datasets and reveals the important differences among them.ResultsWe propose a novel generic consensus clustering technique that applies Formal Concept Analysis (FCA) approach for the consolidation and analysis of clustering solutions derived from several microarray datasets. These datasets are initially divided into groups of related experiments with respect to a predefined criterion. Subsequently, a consensus clustering algorithm is applied to each group resulting in a clustering solution per group.These solutions are pooled together and further analysed by employing FCA which allows extracting valuable insights from the data and generating a gene partition over all the experiments. In order to validate the FCA-enhanced approach two consensus clustering algorithms are adapted to incorporate the FCA analysis. Their performance is evaluated on gene expression data from multi-experiment study examining the global cell-cycle control of fission yeast. The FCA results derived from both methods demonstrate that, although both algorithms optimize different clustering characteristics, FCA is able to overcome and diminish these differences and preserve some relevant biological signals.ConclusionsThe proposed FCA-enhanced consensus clustering technique is a general approach to the combination of clustering algorithms with FCA for deriving clustering solutions from multiple gene expression matrices. The experimental results presented herein demonstrate that it is a robust data integration technique able to produce good quality clustering solution that is representative for the whole set of expression matrices.

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An Integrative Clustering Approach Combining Particle Swarm Optimization and Formal Concept Analysis

Cited by 2 publications

References 21 publications

Elements About Exploratory, Knowledge-Based, Hybrid, and Explainable Knowledge Discovery

Elements About Exploratory, Knowledge-Based, Hybrid, and Explainable Knowledge Discovery

A formal concept analysis approach to consensus clustering of multi-experiment expression data

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