Knowledge-Based Visualization to Support Spatial Data Mining
Knowledge-Based Visualization to Support Spatial Data Mining
Gennady Andrienko and Natalia Andrienko
GMD - German National Research Center for Information Technology Schloss Birlinghoven, Sankt-Augustin, D-53754 Germany gennady.andrienko@gmd.de http://allanon.gmd.de/and/
Abstract. Data mining methods are designed for revealing significant relationships and regularities in data collections. Regarding spatially referenced data, analysis by means of data mining can be aptly complemented by visual exploration of the data presented on maps as well as by cartographic visualization of results of data mining procedures. We propose an integrated environment for exploratory analysis of spatial data that equips an analyst with a variety of data mining tools and provides the service of automated mapping of source data and data mining results. The environment is built on the basis of two existing systems, Kepler for data mining and Descartes for automated knowledge-based visualization. It is important that the open architecture of Kepler allows to incorporate new data mining tools, and the knowledge-based architecture of Descartes allows to automatically select appropriate presentation methods according to characteristics of data mining results. The paper presents example scenarios of data analysis and describes the architecture of the integrated system.
1
Introduction
The notion of Knowledge Discovery in Databases (KDD) denotes the task of revealing significant relationships and regularities in data based on the use of algorithms collectively entitled ”data mining”. The KDD process is an iterative fulfillment of the following steps [6]: 1. Data selection and preprocessing, such as checking for errors, removing outliers, handling missing values, and transformation of formats. 2. Data transformations, for example, discretization of variables or production of derived variables. 3. Selection of a data mining method and adjustment of its parameters. 4. Data mining, i.e.
Gennady Andrienko and Natalia Andrienko
GMD - German National Research Center for Information Technology Schloss Birlinghoven, Sankt-Augustin, D-53754 Germany gennady.andrienko@gmd.de http://allanon.gmd.de/and/
Abstract. Data mining methods are designed for revealing significant relationships and regularities in data collections. Regarding spatially referenced data, analysis by means of data mining can be aptly complemented by visual exploration of the data presented on maps as well as by cartographic visualization of results of data mining procedures. We propose an integrated environment for exploratory analysis of spatial data that equips an analyst with a variety of data mining tools and provides the service of automated mapping of source data and data mining results. The environment is built on the basis of two existing systems, Kepler for data mining and Descartes for automated knowledge-based visualization. It is important that the open architecture of Kepler allows to incorporate new data mining tools, and the knowledge-based architecture of Descartes allows to automatically select appropriate presentation methods according to characteristics of data mining results. The paper presents example scenarios of data analysis and describes the architecture of the integrated system.
1
Introduction
The notion of Knowledge Discovery in Databases (KDD) denotes the task of revealing significant relationships and regularities in data based on the use of algorithms collectively entitled ”data mining”. The KDD process is an iterative fulfillment of the following steps [6]: 1. Data selection and preprocessing, such as checking for errors, removing outliers, handling missing values, and transformation of formats. 2. Data transformations, for example, discretization of variables or production of derived variables. 3. Selection of a data mining method and adjustment of its parameters. 4. Data mining, i.e.
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