Fuzzy multiple support associative classification approach for prediction

Bilal Sowan, Keshav Dehal, Alamgir Hossain

    Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

    1 Citation (Scopus)

    Abstract

    The fact of building an accurate classification and prediction system remains one of the most significant challenges in knowledge discovery and data mining. In this paper, a Knowledge Discovery (KD) framework is proposed; based on the integrated fuzzy approach, more specifically Fuzzy C-Means (FCM) and the new Multiple Support Classification Association Rules (MSCAR) algorithm. MSCAR is considered as an efficient algorithm for extracting both rare and frequent rules using vertical scanning format for the database. Consequently, the adaptation of such a process sufficiently minimized the prediction error. The experimental results regarding two data sets; Abalone and road traffic, show the effectiveness of the proposed approach in building a robust prediction system. The results also demonstrate that the proposed KD framework outperforms the existing prediction systems.
    Original languageEnglish
    Title of host publicationLecture Notes in Artificial Intelligence
    Place of PublicationLondon
    PublisherSpringer
    Pages216-223
    Volume6113
    ISBN (Print)9783642132070
    Publication statusPublished - 2010

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