New Frontiers in Mining Complex Patterns
Springer International Publishing (Verlag)
978-3-030-48860-4 (ISBN)
This book constitutes the refereed post-conference proceedings of the 8th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2019, held in conjunction with ECML-PKDD 2019 in Würzburg, Germany, in September 2019.
The workshop focused on the latest developments in the analysis of complex and massive data sources, such as blogs, event or log data, medical data, spatio-temporal data, social networks, mobility data, sensor data and streams.
A Framework for Pattern Mining and Anomaly Detection in Multi-Dimensional Time Series and Event Logs.- A Heuristic Approach for Sensitive Pattern Hiding with Improved Data Quality.- Interpretable Survival Gradient Boosting Models with Bagged Trees Base Learners.- Neural Hybrid Recommender: Recommendation Needs Collaboration.- Discovering Discriminative Nodes for Classification with Deep Graph Convolutional Methods.- Soft Voting Windowing Ensembles for Learning from Partially Labelled Streams.- Disentangling Aspect and Opinion Words in Sentiment Analysis Using Lifelong PU Learning.- Customer Purchase Behavior Prediction in E-commerce: A Conceptual Framework and Research Agenda.- Hough Transform as a Tool for the Classification of Vehicle Speed Changes in on-road Audio Recordings.
| Erscheinungsdatum | 15.05.2020 |
|---|---|
| Reihe/Serie | Lecture Notes in Artificial Intelligence | Lecture Notes in Computer Science |
| Zusatzinfo | XII, 155 p. 111 illus., 33 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Gewicht | 267 g |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik | |
| Schlagworte | Artificial Intelligence • Clustering • Computer Hardware • Computer Networks • Data Mining • data stream mining • Education • Engineering • Ensemble methods • internetinternet • learning • Linguistics • machine learning • machine learning approaches • multi-task learning • Signal Processing • supervised learning |
| ISBN-10 | 3-030-48860-8 / 3030488608 |
| ISBN-13 | 978-3-030-48860-4 / 9783030488604 |
| Zustand | Neuware |
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