Now showing items 1-3 of 3

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    Grid-based supervised clustering algorithm using greedy and gradient descent methods to build clusters 

    Pornpimol Bungkomkhun; Surapong Auwatanamongkol, advisor (National Institute of Development Administration, 2012)

    Clustering analysis is one of the primary methods of data mining tasks with the objective to understand the natural grouping (or structure) of data objects in a dataset. The clustering tasks aim to segment the entire data set into relatively homogenous subgroups or clusters where the similarities of the data objects within clusters are maximized and the similarities of data objects belonging to different clusters are minimized. For supervised clustering, not only attribute variables of data objects but also the class variable of data objects take ...
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    Materialized views selection using two-phase optimization algorithm 

    Jiratta Phuboon-ob; Raweewan Auepanwiriyakul, advisor (National Institute of Development Administration, 2009)

    A data warehouse (DW) can be defined as a subject-oriented, integrated, nonvolatile and time-variant collection of data, which has value and role for decisionmaking by querying. Queries to DW are critical regarding to their complexity and length. They often access millions of tuples, and involve joins between relations and aggregations. To avoid accessing base tables and increase the speed of queries posed to a DW, we can use some intermediate results from the query processing stored in the DW called materialized views. However, these views have ...
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    Supervised growing neural gas algorithm in clustering analysis 

    Apirak Jirayusakul; Surapong Auwatanamongkol, advisor (National Institute of Development Administration, 2007)