Data Analysis and Mining

doi:10.3390/books978-3-0365-9502-3

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https://mdpi.com/books/pdfview/book/8412

Contributor(s)

Ougiaroglou, Stefanos (editor)

Margaris, Dionisis (editor)

Language

English

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Abstract

The research field of data analysis and mining has attracted the interest of both academia and industry in recent years. This reprint contains 17 papers, which cover different topics of the broad research field of data analysis and mining. Each paper presents new data mining algorithms and techniques, as well as applications of data analysis and mining in real-world domains.

URI

https://directory.doabooks.org/handle/20.500.12854/132388

Keywords

chi-square test; constrained likelihood ratio test; Fisher test; gamma distribution; uniformly most powerful test; key interested frame; commodity video; clustering; deep neural network; frequent subtree; parallel algorithms; data partitioning; load balancing; trust inference; trust propagation; online social network; social network analysis; probabilistic graphical model; message passing; belief propagation; model interpretability; sequential rule mining; non redundant sequential rules; TRuleGrowth; top-k non redundant rules; closed sequential patterns; multivariate time series; deep spatiotemporal information; down-sampling convolution; attention; graph neural network; mobility patterns; social media data; artificial intelligence; tourist clusters; tourist flows; forecasting; univariate; time series; Python; PSF; spam detection; deep learning; semantic similarity; social network security; web analytics; web log mining; clickstream analysis; sequence mining; sequitur; graph techniques; feature subset selection; data mining; educational data mining; machine learning; metaheuristics; artificial neural networks; random decision forests; posttraumatic stress disorder; DSM-V; emergency cesarean section; elective cesarean section; postpartum period; text similarity calculation; passage-level event connection graph; vector tuning; graph embedding; meteorological data mining and machine learning; class imbalance; classification; randomized undersampling; SMOTE oversampling; undersampling using temporal distances; recommender systems; session-based recommendations; e-commerce; data and web mining; item co-occurrence; graph data model; next-item and next-basket recommendations; graph-based recommendations; purchase intent; LSTM-RNN; signal processing; smart device; electromagnetic field; non-ionizing radiation protection; SAR; ANOVA; data science; selection; constraint satisfaction; preprocessing; mobile technology; statistics