Appice / Loglisci / Manco New Frontiers in Mining Complex Patterns

6th International Workshop, NFMCP 2017, Held in Conjunction with ECML-PKDD 2017, Skopje, Macedonia, September 18-22, 2017, Revised Selected Papers
1. Auflage 2018
ISBN: 978-3-319-78680-3
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, Band 10785, 197 Seiten, eBook

Reihe: Lecture Notes in Computer Science

This book features a collection of revised and significantly extended versions of the papers accepted for presentation at the 6th International Workshop on New Frontiers in Mining Complex Patterns, NFMCP 2017, held in conjunction with ECML-PKDD 2017 in Skopje, Macedonia, in September 2017. The book is composed of five parts: feature selection and induction; classification prediction; clustering; pattern discovery; applications. The workshop was aimed at discussing and introducing new algorithmic foundations and representation formalisms in complex pattern discovery. Finally, it encouraged the integration of recent results from existing fields, such as Statistics, Machine Learning and Big Data Analytics.

Zielgruppe

Research

Autoren/Hrsg.

Appice, Annalisa

Loglisci, Corrado

Manco, Giuseppe

Masciari, Elio

Ras, Zbigniew W.

Inhaltsverzeichnis

Learning Association Rules for Pharmacogenomic Studies.- Segment-Removal Based Stuttered Speech Remediation.- Identifying lncRNA-disease Relationships via Heterogeneous Clustering.- Density Estimators for Positive-Unlabeled Learning.- Combinatorial Optimization Algorithms to Mine a Sub-Matrix of Maximal Sum.- A Scaled-Correlation Based Approach for Defining and analyzing functional networks.- Complex Localization in the Multiple Instance Learning Context.- Integrating a Framework for Discovering Alternative App Stores in a Mobile App Monitoring Platform.- Usefulness of Unsupervised Ensemble Learning Methods for Time Series Forecasting of Aggregated or Clustered Load.- Phenotype Prediction with Semi-supervised Classification Trees.- Structuring the Output Space in Multi-label Classification by Using Feature Ranking.- Infinite Mixtures of Markov Chains.- Community-based Semantic Subgroup Discovery.