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Erschienen in:
2021 | OriginalPaper | Buchkapitel
NRCP-Miner: Towards the Discovery of Non-redundant Co-location Patterns
verfasst von : Xuguang Bao, Jinjie Lu, Tianlong Gu, Liang Chang, Lizhen Wang
Erschienen in: Database Systems for Advanced Applications
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Abstract
Co-location pattern mining, which refers to discovering neighboring spatial features in geographic space, is an interesting and important task in spatial data mining. However, in practice, the usefulness of prevalent (interesting) co-location patterns generated by traditional frameworks is strongly limited by their huge amount, which may affect the user’s following decisions. To address this issue, in this demonstration, we present a novel schema, named NRCP-Miner, aiming at the redundancy reduction for prevalent co-location patterns, i.e., discovering non-redundant co-location patterns by utilizing the spatial distribution information of co-location instances. NRCP-Miner can effectively remove the redundant patterns contained in prevalent co-location patterns, thus furtherly assists the user to make the following decisions. We evaluated the efficiency of NRCP-Miner compared with related state-of-the-art approaches.