Data Mining and Knowledge Discovery View Homepage


Ontology type: schema:Periodical     


Journal Info

START YEAR

N/A

PUBLISHER

Springer US

LANGUAGE

en

HOMEPAGE

http://link.springer.com/journal/10618

Recent publications latest 20 shown

  • 2019-04-12 Efficiently mining cohesion-based patterns and rules in event sequences
  • 2019-04-11 Subjectively interesting connecting trees and forests
  • 2019-04-10 Clustering for heterogeneous information networks with extended star-structure
  • 2019-04-09 The decomposed normalized maximum likelihood code-length criterion for selecting hierarchical latent variable models
  • 2019-04-03 Deeply supervised model for click-through rate prediction in sponsored search
  • 2019-03-27 Guest Editorial
  • 2019-03-27 A new class of metrics for learning on real-valued and structured data
  • 2019-03-19 Algorithmic cache of sorted tables for feature selection
  • 2019-03-08 Counts-of-counts similarity for prediction and search in relational data
  • 2019-03-02 Deep learning for time series classification: a review
  • 2019-03 Interpretable multiple data streams clustering with clipped streams representation for the improvement of electricity consumption forecasting
  • 2019-03 DeepRank: improving unsupervised node ranking via link discovery
  • 2019-03 Noise-free latent block model for high dimensional data
  • 2019-03 A review on distance based time series classification
  • 2019-03 Model-free inference of diffusion networks using RKHS embeddings
  • 2019-03 A tutorial on statistically sound pattern discovery
  • 2019-03 Algorithms for approximate subtropical matrix factorization
  • 2019-03 Multi-target prediction: a unifying view on problems and methods
  • 2019-02-20 Dynamic detection of anomalous regions within distributed acoustic sensing data streams using locally stationary wavelet time series
  • 2019-02-20 Unsupervised dimensionality reduction versus supervised regularization for classification from sparse data
  • JSON-LD is the canonical representation for SciGraph data.

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