Knowledge discovery and data mining: challenges and realities
Knowledge discovery and data mining (KDD) is dedicated to exploring meaningful information from a large volume of data. Knowledge Discovery and Data Mining: Challenges and Realities is the most comprehensive reference publication for researchers and real-world data mining practitioners to advance knowledge discovery from low-quality data. This Premier Reference Source presents in-depth experiences and methodologies, providing theoretical and empirical guidance to users who have suffered from underlying, low-quality data. International experts in the field of data mining have contributed all-inclusive chapters focusing on interdisciplinary collaborations among data quality, data processing, data mining, data privacy, and data sharing.
89 pages matching data mining challenges and realities in this book
Results 1-3 of 89
What people are saying - Write a review
We haven't found any reviews in the usual places.
Other editions - View all
algorithm analysis applications approach associated business impact captioning accuracy CaRBS case-based reasoning chapter charge-off classification accuracy Computer concepts condition attributes considered correlations data items data mining database dataset decision rules decision tree defined Dempster-Shafer theory detection developed epistasis error rate estimation evaluation expert Figure function gene ontology genetic graph IEEE image captioning image mining information entropy input instances interaction International Conference knowledge discovery machine learning MAGIC method methodology missing values model quality multimedia negative node objects ontology engineering Ontology learning outlier P-reduct parameters patterns performance Perner positive predictions predictive model problem protein ontology regions resampling risk samples SDT tree chain selected Seliya semantic semisupervised classification semisupervised clustering simplex plot SNPs software quality software quality modeling specific statistical subset table of confusion technical metrics techniques tion Type I error variable BOEs VPRS ziprasidone