Parallel Evolutionary Biclustering of Short-term Electric Energy Consumption

Pinto-Roa, Diego P.; Medina, Hernán; Román, Federico; García-Torres, Miguel; Divina, Federico; Gómez-Vela, Francisco; Morales, Félix; Velázquez, Gustavo; Daumas, Federico; VázquezNoguera, José L.; Ayala, Carlos Sauer; Gardel-Sotomayor, Pedro E.

doi:10.5121/csit.2021.111110

Cited by 3 publications

(2 citation statements)

References 48 publications

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“…This is relevant for domains with an assumed ordering, such as time series datasets (where columns identify ordered time points). Examples of contiguous biclusters are seen in diverse scientific domains such as gene expression data, where the temporal change in expression patterns is used to monitor complex biological processes such as disease progression and drug responses [ 86 ], fMRI data, where biclusters measure an activity pattern in several regions [ 26 , 87 ], and resources utilization, measured over time (usually expressed in the columns) [ 69 , 88 ]. In this context, the contiguous column bicluster (CC-Bicluster) was defined [ 11 ] and expanded [ 73 , 74 ], with interpretive advantages.…”

Section: Biclustermentioning

confidence: 99%

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Biclustering data analysis: a comprehensive survey

Castanho,

Aidos,

Madeira

2024

Briefings in Bioinformatics

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Biclustering, the simultaneous clustering of rows and columns of a data matrix, has proved its effectiveness in bioinformatics due to its capacity to produce local instead of global models, evolving from a key technique used in gene expression data analysis into one of the most used approaches for pattern discovery and identification of biological modules, used in both descriptive and predictive learning tasks. This survey presents a comprehensive overview of biclustering. It proposes an updated taxonomy for its fundamental components (bicluster, biclustering solution, biclustering algorithms, and evaluation measures) and applications. We unify scattered concepts in the literature with new definitions to accommodate the diversity of data types (such as tabular, network, and time series data) and the specificities of biological and biomedical data domains. We further propose a pipeline for biclustering data analysis and discuss practical aspects of incorporating biclustering in real-world applications. We highlight prominent application domains, particularly in bioinformatics, and identify typical biclusters to illustrate the analysis output. Moreover, we discuss important aspects to consider when choosing, applying, and evaluating a biclustering algorithm. We also relate biclustering with other data mining tasks (clustering, pattern mining, classification, triclustering, N-way clustering, and graph mining). Thus, it provides theoretical and practical guidance on biclustering data analysis, demonstrating its potential to uncover actionable insights from complex datasets.

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Section: Biclustermentioning

confidence: 99%

“…Biclustering is also used to identify patterns of resources utilization . This includes the temporal analysis of a diversity of attributes such as water [ 69 ], energy consumption [ 88 ], or urban mobility patterns [ 68 , 254 , 268 ].…”

Section: Applicationsmentioning

confidence: 99%

Biclustering data analysis: a comprehensive survey

Castanho,

Aidos,

Madeira

2024

Briefings in Bioinformatics

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G-bic: generating synthetic benchmarks for biclustering

Castanho,

Lobo,

Henriques

et al. 2023

BMC Bioinformatics

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Background Biclustering is increasingly used in biomedical data analysis, recommendation tasks, and text mining domains, with hundreds of biclustering algorithms proposed. When assessing the performance of these algorithms, more than real datasets are required as they do not offer a solid ground truth. Synthetic data surpass this limitation by producing reference solutions to be compared with the found patterns. However, generating synthetic datasets is challenging since the generated data must ensure reproducibility, pattern representativity, and real data resemblance. Results We propose G-Bic, a dataset generator conceived to produce synthetic benchmarks for the normative assessment of biclustering algorithms. Beyond expanding on aspects of pattern coherence, data quality, and positioning properties, it further handles specificities related to mixed-type datasets and time-series data.G-Bic has the flexibility to replicate real data regularities from diverse domains. We provide the default configurations to generate reproducible benchmarks to evaluate and compare diverse aspects of biclustering algorithms. Additionally, we discuss empirical strategies to simulate the properties of real data. Conclusion G-Bic is a parametrizable generator for biclustering analysis, offering a solid means to assess biclustering solutions according to internal and external metrics robustly.

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Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study

et al. 2022

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Correctly defining and grouping electrical feeders is of great importance for electrical system operators. In this paper, we compare two different clustering techniques, K-means and hierarchical agglomerative clustering, applied to real data from the east region of Paraguay. The raw data were pre-processed, resulting in four data sets, namely, (i) a weekly feeder demand, (ii) a monthly feeder demand, (iii) a statistical feature set extracted from the original data and (iv) a seasonal and daily consumption feature set obtained considering the characteristics of the Paraguayan load curve. Considering the four data sets, two clustering algorithms, two distance metrics and five linkage criteria a total of 36 models with the Silhouette, Davies–Bouldin and Calinski–Harabasz index scores was assessed. The K-means algorithms with the seasonal feature data sets showed the best performance considering the Silhouette, Calinski–Harabasz and Davies–Bouldin validation index scores with a configuration of six clusters.

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Parallel Evolutionary Biclustering of Short-term Electric Energy Consumption

Cited by 3 publications

References 48 publications

Biclustering data analysis: a comprehensive survey

Biclustering data analysis: a comprehensive survey

G-bic: generating synthetic benchmarks for biclustering

Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study

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