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Interpretable market segmentation on high dimension data
(M D P I AG, 2018-09-17)
[Abstract] Obtaining relevant information from the vast amount of data generated by interactions in a market or, in general, from a dyadic dataset, is a broad problem of great interest both for industry and academia. Also, ...
Regression Tree Based Explanation for Anomaly Detection Algorithm
(MDPI AG, 2020-08-18)
[Abstract]
This work presents EADMNC (Explainable Anomaly Detection on Mixed Numerical and Categorical spaces), a novel approach to address explanation using an anomaly detection algorithm, ADMNC, which provides accurate ...
Sustainable personalisation and explainability in Dyadic Data Systems
(2022)
[Abstract]: Systems that rely on dyadic data, which relate entities of two types together, have become ubiquitously used in fields such as media services, tourism business, e-commerce, and others. However, these systems ...
Scalable Feature Selection Using ReliefF Aided by Locality-Sensitive Hashing
(Wiley, 2021)
[Abstract] Feature selection algorithms, such as ReliefF, are very important for processing high-dimensionality data sets. However, widespread use of popular and effective such algorithms is limited by their computational ...
Case Study of Anomaly Detection and Quality Control of Energy Efficiency and Hygrothermal Comfort in Buildings
(2019)
[Abstract] The aim of this work is to propose different statistical and machine learning methodologies for identifying
anomalies and control the quality of energy efficiency and hygrothermal comfort in buildings. ...
A scalable decision-tree-based method to explain interactions in dyadic data
(Elsevier, 2019-12)
[Abstract]: Gaining relevant insight from a dyadic dataset, which describes interactions between two entities, is an open problem that has sparked the interest of researchers and industry data scientists alike. However, ...
A novel framework for generic Spark workload characterization and similar pattern recognition using machine learning
(Elsevier, 2024-07)
[Abstract]: Comprehensive workload characterization plays a pivotal role in comprehending Spark applications, as it enables the analysis of diverse aspects and behaviors. This understanding is indispensable for devising ...
Fast Distributed kNN Graph Construction Using Auto-tuned Locality-sensitive Hashing
(Association for Computing Machinery, 2020)
[Abstract]: The k-nearest-neighbors (kNN) graph is a popular and powerful data structure that is used in various areas of Data Science, but the high computational cost of obtaining it hinders its use on large datasets. ...
Large scale anomaly detection in mixed numerical and categorical input spaces
(Elsevier, 2019)
[Abstract]: This work presents the ADMNC method, designed to tackle anomaly detection for large-scale problems with a mixture of categorical and numerical input variables. A flexible parametric probability measure is ...
Fast anomaly detection with locality-sensitive hashing and hyperparameter autotuning
(Elsevier, 2022-08)
[Abstract]: This paper presents LSHAD, an anomaly detection (AD) method based on Locality Sensitive Hashing (LSH), capable of dealing with large-scale datasets. The resulting algorithm is highly parallelizable and its ...