Quantitative association rules over incomplete data

Vincent To Yee Ng, John Lee

Research output: Journal article publicationConference articleAcademic researchpeer-review

7 Citations (Scopus)

Abstract

This paper explores the use of principle component analysis (PCA) to estimate missing values during the mining of quantitative association rules. An example of such association may be `15% of customers spend $100-$300 every month will have 2 cable outlets at home'. In our algorithm, instead of imputing missing values before the mining process, we propose to integrate the imputation step within the process. The idea is to reduce the unnecessary imputation effort and to improve the overall performance. First, only attributes with enough support counts and with missing values are required to perform imputations. Thus, effort will not be wasted on unimportant attributes. Further, rather than estimating the actual value of a missing data, the possible range of the value is guessed. This will not affect the resultant quantitative association rules much but will cut down the guessing effort.
Original languageEnglish
Pages (from-to)2821-2826
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume3
Publication statusPublished - 1 Dec 1998
EventProceedings of the 1998 IEEE International Conference on Systems, Man, and Cybernetics. Part 3 (of 5) - San Diego, CA, United States
Duration: 11 Oct 199814 Oct 1998

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture

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