FRCU - GIBD : Grupo de Investigación en Bases de Datos - Artículos

Permanent URI for this collectionhttp://48.217.138.120/handle/20.500.12272/792

Browse

Subject: search.filters.subject.Knowledge discovery process

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Knowledge discovery process for detection of spatial outliers
    (2018) Rottoli, Giovanni Daián; Merlino, Hernán Daniel; García Martínez, Ramón
    Detection of spatial outliers is a spatial data mining task aimed at discovering data observations that differ from other data observations within its spatial neighborhood. Some considerations that depend on the problem domain and data characteristics have to be taken into account for the selection of the data mining algorithms to be used in each data mining project. This massive amount of possible algorithm combinations makes it necessary to design a knowledge discovery process for detection of local spatial outliers in order to perform this activity in a standardized way. This work provides a proposal for this knowledge discovery process based on the Knowledge Discovery in Database process (KDD) and a proof of concept of this design using real world data.
  • Thumbnail Image
    Item
    Co-location rules discovery process focused on reference spatial features using decision tree learning
    (2017) Rottoli, Giovanni Daián; Merlino, Hernán Daniel; García Martínez, Ramón
    The co-location discovery process serves to find subsets of spatial features frequently located together. Many algorithms and methods have been designed in recent years; however, finding this kind of patterns around specific spatial features is a task in which the existing solutions provide incorrect results. Throughout this paper we propose a knowledge discovery process to find co-location patterns focused on reference features using decision tree learning algorithms on transactional data generated using maximal cliques. A validation test of this process is provided.