Journal - Volume I No.1 September 2014 , Special Issue-1

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  • Title

    :

    FINDING PROBABILISTIC PREVALENT COLOCATIONS IN SPATIALLY UNCERTAIN DATA MINING IN AGRICULTURE  USING FUZZY LOGICS

    Authors

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    Ms.Latha.R 1 , Gunasekaran E 2

    Keywords

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    Data Mining , Fuzzy Logics .

    Issue Date

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    September - 2014

    Abstract

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    A spatial collocation pattern is a group of spatial features whose instances are frequently located together in geographic space. Discovering collocations has many useful applications. For example, collocated plants pieces discovered from plant distribution datasets can contribute to the analysis of plant geography, phytosociology studies, and plant protection recommendations. In this paper, we study the collocation mining problem in the context of uncertain data, as the data generated from a wide range of data sources are in here only uncertain. One straight forward method to mine the prevalent collocations in a spatially uncertain data set is to simply compute the expected participation index of a candidate and decide if it exceeds a minimum prevalence threshold. Although this definition has been widely adopted, it misses important information about the confidence which can be associated with the participation index of a colocation. We propose another definition, probabilistic prevalent colocations, trying to find all the collocations that are likely to be prevalent in a randomly generated possible world. Finding probabilistic prevalent colocations (PPCs) turn out to be difficult. First, we propose pruning strategies for candidates to reduce the amount of computation of the probabilistic participation index values. Next, we design an improved dynamic programming algorithm for identifying candidates. This algorithm is suitable for parallel computation, and approximate computation. Finally, the effectiveness and efficiency of the methods proposed as well as the pruning strategies and the optimization techniques are verified by extensive experiments with “real þ synthetic” spatially uncertain data sets.

    Page(s)

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    133 -  139

    ISSN

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    2347- 4734

    Source

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    Vol. 1, No.1 - Special Issue-1

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