rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Data Science, Learning by Latent Structures, and Knowledge Discovery
  name:
    name part: Lausen, Berthold.
    role:
      role term: editor.
  name:
    name part: Krolak-Schwerdt, Sabine.
    role:
      role term: editor.
  name:
    name part: Böhmer, Matthias.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2015
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XXII, 560 p. 145 illus., 56 illus. in color. online resource.
  abstract: This volume comprises papers dedicated to data science and the extraction of knowledge from many types of data: structural, quantitative, or statistical approaches for the analysis of data; advances in classification, clustering, and pattern recognition methods; strategies for modeling complex data and mining large data sets; applications of advanced methods in specific domains of practice. The contributions offer interesting applications to various disciplines such as psychology, biology, medical and health sciences; economics, marketing, banking, and finance; engineering; geography and geology; archeology, sociology, educational sciences, linguistics, and musicology; library science. The book contains the selected and peer-reviewed papers presented during the European Conference on Data Analysis (ECDA 2013) which was jointly held by the German Classification Society (GfKl) and the French-speaking Classification Society (SFC) in July 2013 at the University of Luxembourg.
  table of contents: Part I Invited Papers: Modernising Official Statistics - A Complex Challenge -- A New Supervised Classification of Credit Approval Data via the Hybridized RBF Neural Network Model Using Information Complexity -- Finding the Number of Disparate Clusters with Background Contamination -- Clustering of Solar Irradiance -- Part II Data Science and Clustering: Factor Analysis of Local Formalism -- Recent progress in Complex Network Analysis – Models -- Recent Progress in Complex Network Analysis – Results.- Similarity Measures of Concept Lattices -- Flow-Based Dissimilarities: Shortest Path, Commute Time, Max-Flow and Free Energy -- Resampling Techniques in Cluster Analysis – Is Subsampling Better Than Bootstrapping? -- On-Line Clustering of Functional Boxplots for Monitoring Multiple Streaming Time Series -- Smooth Tests of Fit for Gaussian Mixtures -- Part III Machine Learning and Knowledge Discovery: P2P RVM for Distributed Classification -- Selecting a Multi-Label Classification Method for an Interactive System -- Visual Analysis of Topics in Twitter Based on Co-Evolution of Terms -- Incremental Weighted Naive Bayes Classifiers for Data Stream -- SVM Ensembles are Better When Different Kernel Types are Combined -- Part IV Data Analysis in Marketing: Ratings-Based Versus Choice-Based Conjoint Analysis for Predicting Choices -- A Statistical Software Package for Image Data Analysis in Marketing -- The Bass Model as Integrative Diffusion Model - A Comparison of Parameter Influences -- Preference Measurement in Complex Product Development – A Comparison of Two-Staged SEM Approaches -- Combination of Distances and Image Features for Clustering Image Data Bases -- A Game Theoretic Product Design Approach Considering Stochastic Partworth Functions -- Key Success-Determinants of Crowdfunded Projects – An Exploratory Analysis -- Preferences Interdependence among Family Members – Case III/APIM Approach -- Part V Data Analysis in Biostatistics and Bioinformatics: Evaluation for …
  note: edited by Berthold Lausen, Sabine Krolak-Schwerdt, Matthias Böhmer.
  subject:
    topic: Statistics
  subject:
    topic: Marketing
  subject:
    topic: Operations research
  subject:
    topic: Decision making
  subject:
    topic: Data mining
  subject:
    topic: Biostatistics
  subject:
    topic: Psychometrics
  subject:
    topic: Statistics
  subject:
    topic: Statistics, general
  subject:
    topic: Marketing
  subject:
    topic: Psychometrics
  subject:
    topic: Biostatistics
  subject:
    topic: Data Mining and Knowledge Discovery
  subject:
    topic: Operation Research/Decision Theory
  classification: QA276-280
  classification: 519.5
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Studies in Classification, Data Analysis, and Knowledge Organization
  identifier: 9783662449837
  identifier: http://dx.doi.org/10.1007/978-3-662-44983-7
  record info:
    record creation date: 150506
    record change date: 20151204153800.0
    record identifier: 978-3-662-44983-7