Nonparametric recursive aggregation process

summary:In this work we introduce a nonparametric recursive aggregation process called Multilayer Aggregation (MLA). The name refers to the fact that at each step the results from the previous one are aggregated and thus, before the final result is derived, the initial values are subjected to several layers of aggregation. Most of the conventional aggregation operators, as for instance weighted mean, combine numerical values according to a vector of weights (parameters). Alternatively, the MLA operators apply recursively over the input values a vector of aggregation operators. Consequently, a sort of unsupervised self-tuning aggregation process is induced combining the individual values in a certain fashion determined by the choice of aggregation operators

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

Background: Presently, 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. Results: We 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. Conclusions: The 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

Context-Aware Performance Benchmarking of a Fleet of Industrial Assets

Industrial assets are instrumented with sensors, connected and continuously monitored. The collected data, generally in form of time-series, is used for corrective and preventive maintenance. More advanced exploitation of this data for very diverse purposes, e.g. identifying underperformance, operational optimization or predictive maintenance, is currently an active area of research. The general methods used to analyze the time-series lead to models that are either too simple to be used in complex operational contexts or too difficult to be generalized to the whole fleet due to their asset-specific nature. Therefore, we have conceived an alternative methodology allowing to better characterize the operational context of an asset and quantify the impact on its performance. The proposed methodology allows to benchmark and profile fleet assets in a context-aware fashion, is applicable in multiple domains (even without ground truth). The methodology is evaluated on real-world data coming from a fleet of wind turbines and compared to the standard approach used in the domain. We also illustrate how the asset performance (in terms of energy production) is influenced by the operational context (in terms of environmental conditions). Moreover, we investigate how the same operational context impacts the performance of the different assets in the fleet and how groups of similarly behaving assets can be determined