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An Investigation in Efficient Spatial Patterns Mining

Wang, Lizhen (2008) An Investigation in Efficient Spatial Patterns Mining. Doctoral thesis, University of Huddersfield.

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    Abstract

    The technical progress in computerized spatial data acquisition and storage results
    in the growth of vast spatial databases. Faced with large amounts of increasing spatial
    data, a terminal user has more difficulty in understanding them without the helpful
    knowledge from spatial databases. Thus, spatial data mining has been brought under
    the umbrella of data mining and is attracting more attention.
    Spatial data mining presents challenges. Differing from usual data, spatial data includes
    not only positional data and attribute data, but also spatial relationships among
    spatial events. Further, the instances of spatial events are embedded in a continuous
    space and share a variety of spatial relationships, so the mining of spatial patterns demands
    new techniques.
    In this thesis, several contributions were made. Some new techniques were proposed,
    i.e., fuzzy co-location mining, CPI-tree (Co-location Pattern Instance Tree),
    maximal co-location patterns mining, AOI-ags (Attribute-Oriented Induction based on Attributes’
    Generalization Sequences), and fuzzy association prediction. Three algorithms
    were put forward on co-location patterns mining: the fuzzy co-location mining algorithm,
    the CPI-tree based co-location mining algorithm (CPI-tree algorithm) and the orderclique-
    based maximal prevalence co-location mining algorithm (order-clique-based algorithm).
    An attribute-oriented induction algorithm based on attributes’ generalization sequences
    (AOI-ags algorithm) is further given, which unified the attribute thresholds and
    the tuple thresholds. On the two real-world databases with time-series data, a fuzzy association
    prediction algorithm is designed. Also a cell-based spatial object fusion algorithm
    is proposed. Two fuzzy clustering methods using domain knowledge were proposed:
    Natural Method and Graph-Based Method, both of which were controlled by a
    threshold. The threshold was confirmed by polynomial regression. Finally, a prototype
    system on spatial co-location patterns’ mining was developed, and shows the relative
    efficiencies of the co-location techniques proposed
    The techniques presented in the thesis focus on improving the feasibility, usefulness,
    effectiveness, and scalability of related algorithm. In the design of fuzzy co-location
    Abstract
    mining algorithm, a new data structure, the binary partition tree, used to improve the
    process of fuzzy equivalence partitioning, was proposed. A prefix-based approach to
    partition the prevalent event set search space into subsets, where each sub-problem can
    be solved in main-memory, was also presented. The scalability of CPI-tree algorithm is
    guaranteed since it does not require expensive spatial joins or instance joins for identifying
    co-location table instances. In the order-clique-based algorithm, the co-location table
    instances do not need be stored after computing the Pi value of corresponding colocation,
    which dramatically reduces the executive time and space of mining maximal colocations.
    Some technologies, for example, partitions, equivalence partition trees, prune
    optimization strategies and interestingness, were used to improve the efficiency of the
    AOI-ags algorithm. To implement the fuzzy association prediction algorithm, the “growing
    window” and the proximity computation pruning were introduced to reduce both I/O and
    CPU costs in computing the fuzzy semantic proximity between time-series.
    For new techniques and algorithms, theoretical analysis and experimental results
    on synthetic data sets and real-world datasets were presented and discussed in the thesis.

    Item Type: Thesis (Doctoral)
    Subjects: Q Science > Q Science (General)
    Q Science > QA Mathematics > QA75 Electronic computers. Computer science
    Schools: School of Computing and Engineering
    Related URLs:
    Depositing User: Sara Taylor
    Date Deposited: 18 Dec 2008 15:42
    Last Modified: 28 Jul 2010 19:29
    URI: http://eprints.hud.ac.uk/id/eprint/2978

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