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A clustering rule-based approach to predictive modeling

Published: 15 April 2010 Publication History

Abstract

Recent discoveries using rule-based classifiers and pre-learning data clustering have helped improve classification accuracy in predictive modeling tasks. This research introduces a unique approach which combines the above techniques and studies its predictive effects. The algorithm presented in this research, a Clustering Rule-based Algorithm (CRA), first clusters the original training set using an Expectation Maximization (EM) algorithm. Then, a separate Classification and Regression Tree (CART) is trained on each individual cluster. To obtain an upper-bound on accuracy, each test instance is evaluated against all of the rules produced by each separate Tree, to determine if there exists a rule produced by one of the Trees which correctly classifies the test instance. This study reveals that a predictive accuracy of 100% was achievable. Moreover, this approach exploits the advantages of supervised and unsupervised learning to produce a more powerful and more accurate predictive model.

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  • (2019)Context-aware rule learning from smartphone data: survey, challenges and future directionsJournal of Big Data10.1186/s40537-019-0258-46:1Online publication date: 31-Oct-2019
  • (2013)A learning framework for the optimization and automation of document binarization methodsComputer Vision and Image Understanding10.1016/j.cviu.2012.11.003117:3(269-280)Online publication date: 1-Mar-2013

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    cover image ACM Conferences
    ACMSE '10: Proceedings of the 48th annual ACM Southeast Conference
    April 2010
    488 pages
    ISBN:9781450300643
    DOI:10.1145/1900008
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    Published: 15 April 2010

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    Author Tags

    1. clustering
    2. data mining
    3. rule-based classifier

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    ACM SE '10: ACM Southeast Regional Conference
    April 15 - 17, 2010
    Mississippi, Oxford

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    ACMSE '10 Paper Acceptance Rate 48 of 94 submissions, 51%;
    Overall Acceptance Rate 502 of 1,023 submissions, 49%

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    View all
    • (2019)Context-aware rule learning from smartphone data: survey, challenges and future directionsJournal of Big Data10.1186/s40537-019-0258-46:1Online publication date: 31-Oct-2019
    • (2013)A learning framework for the optimization and automation of document binarization methodsComputer Vision and Image Understanding10.1016/j.cviu.2012.11.003117:3(269-280)Online publication date: 1-Mar-2013

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