Scalable learning technologies for big data mining

Gerard De Melo, Aparna S. Varde

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

As data expands into big data, enhanced or entirely novel data mining algorithms often become necessary. The real value of big data is often only exposed when we can adequately mine and learn from it. We provide an overview of new scalable techniques for knowledge discovery. Our focus is on the areas of cloud data mining and machine learning, semi-supervised processing, and deep learning. We also give practical advice for choosing among different methods and discuss open research problems and concerns.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications - 20th International Conference, DASFAA 2015, Hanoi, Vietnam, April 20-23, 2015 Proceedings, Part II
EditorsMuhammad Aamir Cheema, Matthias Renz, Cyrus Shahabi, Xiaofang Zhou
PublisherSpringer Verlag
ISBN (Print)9783319181226
StatePublished - 1 Jan 2015
Event20th International Conference on Database Systems for Advanced Applications, DASFAA 2015 - Hanoi, Viet Nam
Duration: 20 Apr 201523 Apr 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9050
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other20th International Conference on Database Systems for Advanced Applications, DASFAA 2015
CountryViet Nam
CityHanoi
Period20/04/1523/04/15

Keywords

  • Big Data
  • Cloud Data Mining
  • Deep Learning
  • Semi-Supervised Learning

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