Approximate frequent itemset mining in the presence of random noise

Hong Cheng, Philip S. Yu, Jiawei Han

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Frequent itemset mining has been a focused theme in data mining research and an important first step in the analysis of data arising in a broad range of applications. The traditional exact model for frequent itemset requires that every item occur in each supporting transaction. However, real application data is usually subject to random noise or measurement error, which poses new challenges for the efficient discovery of frequent itemset from the noisy data. Mining approximate frequent itemset in the presence of noise involves two key issues: the definition of a noise-tolerant mining model and the design of an efficient mining algorithm. In this chapter, we will give an overview of the approximate itemset mining algorithms in the presence of random noise and examine several noise-tolerant mining approaches.

Original languageEnglish (US)
Title of host publicationSoft Computing for Knowledge Discovery and Data Mining
PublisherSpringer US
Pages363-389
Number of pages27
ISBN (Print)9780387699349
DOIs
StatePublished - Dec 1 2008

Cite this

Cheng, H., Yu, P. S., & Han, J. (2008). Approximate frequent itemset mining in the presence of random noise. In Soft Computing for Knowledge Discovery and Data Mining (pp. 363-389). Springer US. https://doi.org/10.1007/978-0-387-69935-6_15