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Recent advances in mining patterns from complex data

  • Annalisa Appice
  • Michelangelo Ceci
  • Corrado Loglisci
  • Giuseppe Manco
  • Elio Masciari
Article
  • 402 Downloads

Nowadays data mining and knowledge discovery are advanced research fields with numerous algorithms and studies to extract patterns and models from complex data sources like blogs, event or log data, biological data, spatio-temporal data, social networks, mobility data, sensor data and streams, and so on. Contrary to classical data mining approaches, which look for patterns in tabular data, numerous recent studies focus on data with a complex structure spanning from structured to multimedia and spatial or spatio-temporal data. As such, they put particular emphasis on storing, managing and mining complex interactions among entities in distributed and heterogeneous environments. In terms of scientific research, mining patterns from complex data has been focusing on developing specialized techniques and algorithms, which preserve the informative richness of data and allow us to efficiently and efficaciously identify complex information units present in such data.

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Keywords

Data mining Complex data Complex pattern discovery 

References

  1. Diamantini, C., Genga, L., & Potena, D. (2016). Behavioral process mining for unstructured processes. J. Intell. Inf. Syst. doi: 10.1007/s10844-016-0394-7.
  2. Ferilli, S. (2016). Predicate invention-based specialization in inductive logic programming. J. Intell. Inf. Syst. doi: 10.1007/s10844-016-0412-9.
  3. Madjarov, G., Gjorgjevikj, D., Dimitrovski, I., & Dzeroski, S. (2016). The use of data-derived label hierarchies in multi-label classification. J. Intell. Inf. Syst. doi: 10.1007/s10844-016-0405-8.
  4. Minervini, P., d’Amato, C., & Fanizzi, N. (2016). Efficient energy-based embedding models for link prediction in knowledge graphs. J. Intell. Inf. Syst. doi: 10.1007/s10844-016-0414-7.
  5. Saia, R., Boratto, L., & Carta, S. (2016). A semantic approach to remove incoherent items from a user profile and improve the accuracy of a recommender system. J. Intell. Inf. Syst. doi: 10.1007/s10844-016-0406-7.
  6. Samet, A., Lefevre, E., & Yahia, S.B. (2016). Evidential data mining: Precise support and confidence. J. Intell. Inf. Syst. doi: 10.1007/s10844-016-0396-5.
  7. Sen, E., Toroslu, I.H., & Karagoz, P. (2016). Improving the prediction of page access by using semantically enhanced clustering. J. Intell. Inf. Syst. doi: 10.1007/s10844-016-0398-3.

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Annalisa Appice
    • 1
  • Michelangelo Ceci
    • 1
  • Corrado Loglisci
    • 1
  • Giuseppe Manco
    • 2
  • Elio Masciari
    • 2
  1. 1.Dipartimento di InformaticaUniversità degli Studi di Bari “Aldo Moro”BariItaly
  2. 2.ICAR-Consiglio Nazionale delle RicercheRendeItaly

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