Knowledge-Based Visualization to Support Spatial Data Mining

Powerful Essays
Topics: 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.



References: 1. Andrienko, G., and Andrienko, N.: Intelligent Visualization and Dynamic Manipulation: Two Complementary Instruments to Support Data Exploration with GIS. In: Proceedings of AVI’98: Advanced Visual Interfaces Int. Working Conference (L’Aquila Italy, May 24-27, 1998), ACM Press (1998) 66-75 2. Brodley, C.: Addressing the Selective Superiority Problem: Automatic Algorithm / Model Class Selection. In: Machine Learning: Proceedings of the 10th International Conference, University of Massachusetts, Amherst, June 27-29, 1993. San Mateo, Calif.: Morgan Kaufmann (1993) 17-24 3. Cook, D., Symanzik, J., Majure, J.J., and Cressie, N.: Dynamic Graphics in a GIS: More Examples Using Linked Software. Computers and Geosciences, 23 (1997) 371-385 4. Gama, J. and Brazdil, P.: Characterization of Classification Algorithms. In: Progress in Artificial Intelligence, Lecture Notes in Artificial Intelligence, Vol.990. Springer-Verlag: Berlin (1995) 189-200 5. Gebhardt, F.: Finding Spatial Clusters. In: Principles of Data Mining and Knowledge Discovery PKDD97, Lecture Notes in Computer Science, Vol.1263. SpringerVerlag: Berlin (1997) 277-287 6. Fayyad, U., Piatetsky-Shapiro, G., and Smyth, P.: The KDD Process for Extracting Useful Knowledge from Volumes of Data. Communications of the ACM, 39 (1996), 27-34 7. John, G.H.: Enhancements to the Data Mining Process. PhD dissertation, Stanford University. Available at the URL http://robotics.stanford.edu/∼gjohn/ (1997) 8. Kodratoff, Y.: From the art of KDD to the science of KDD. Research report 1096, Universite de Paris-sud (1997) 9. Koperski, K., Han, J., and Stefanovic, N.: An Efficient Two-Step Method for Classification of Spatial Data. In: Proceedings SDH98, Vancouver, Canada: International Geographical Union (1998) 45-54 10. MacDougall, E.B.: Exploratory Analysis, Dynamic Statistical Visualization, and Geographic Information Systems. Cartography and Geographic Information Systems, 19 (1992) 237-246 11. Wrobel, S., Wettschereck, D., Sommer, E., and Emde, W.: Extensibility in Data Mining Systems. In Proceedings of KDD96 2nd International Conference on Knowledge Discovery and Data Mining. AAAI Press (1996) 214-219

You May Also Find These Documents Helpful

  • Powerful Essays

    Chapter 3 – Data Visualization Chapter 4 – Summary Statistics Data Mining for Business Intelligence Shmueli, Patel & Bruce © Galit Shmueli and Peter Bruce 2010 Data Visualization • “A picture is worth a thousand words” • Data visualization and summary statistics help condense data • Effective presentation • Supports data cleaning (identify missing values, outliers, incorrect values, duplicates) and exploring (combine some groups) • Helps identify suitable variables • Mandatory initial step for…

    • 1091 Words
    • 12 Pages
    Powerful Essays
  • Better Essays

    Managing Knowledge in organizations G.O. KAYODE-ADEDEJI SCHOOL OF ENGINEERING, DESIGN and TECHNOLOGY UNIVERSITY OF BRADFORD G.O.Kayode-Adedeji@bradford.ac.uk 2011 [Type the company name] 1/1/2011 Contents Introduction 2 KNOWLEDGE MANAGEMENT VS INFORMATION MANAGEMENT 5 KNOWLEDGE MANAGEMENT CONTROVERSIES 5 POSSIBLE CONSTRAINTS IN THE IMPLEMENTATION OF A KNOWLEDGE MANAGEMENT PROGRAM 6 CASE STUDY ON THE SUCCESSFUL IMPLEMENTATION OF KM: 6 THE EVOLUTION OF KM AT BUCKMAN LABORATORIES…

    • 5375 Words
    • 22 Pages
    Better Essays
  • Powerful Essays

    Spatial Data

    • 10270 Words
    • 42 Pages

    any browser and on Windows platform. CHAPTER 2 SYSTEM ANALYSIS 2.1 INTRODUCTION Systems analysis is a process of collecting factual data, understand the processes involved, identifying problems and recommending feasible suggestions for improving the system functioning. This involves studying the business processes, gathering operational data, understand the information flow, finding out bottlenecks and evolving solutions for overcoming the weaknesses of the system so as to achieve the…

    • 10270 Words
    • 42 Pages
    Powerful Essays
  • Powerful Essays

    Data Mining

    • 2055 Words
    • 9 Pages

    Chapter 1 Exercises 1. What is data mining? In your answer, address the following: Data mining refers to the process or method that extracts or \mines" interesting knowledge or patterns from large amounts of data. (a) Is it another hype? Data mining is not another hype. Instead, the need for data mining has arisen due to the wide availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge. Thus, data mining can be viewed as the result of…

    • 2055 Words
    • 9 Pages
    Powerful Essays
  • Powerful Essays

    Advancing Statewide Spatial Data Infrastructures in Support of the National Spatial Data Infrastructure (NSDI) Strategic Planning Process Map For use by all Stakeholders in the Geospatial Community Produced by NSGIC for the Federal Geographic Data Committee (FGDC) March 2006 Advancing Statewide Spatial Data Infrastructures in Support of the National Spatial Data Infrastructure (NSDI) Strategic Planning Process Map For use by all Stakeholders in the Geospatial Community This…

    • 1762 Words
    • 8 Pages
    Powerful Essays
  • Powerful Essays

    data mining

    • 1589 Words
    • 9 Pages

    Components of DSS (Decision Support System) Data Store – The DSS Database Data Extraction and Filtering End-User Query Tool End User Presentation Tools Operational Stored in Normalized Relational Database Support transactions that represent daily operations (Not Query Friendly) Differences with DSS 3 Main Differences Time Span Granularity Dimensionality Operational DSS Time span Real time Historic Current transaction Short time frame Long time frame Specific Data facts Patterns Granularity Specific…

    • 1589 Words
    • 9 Pages
    Powerful Essays
  • Good Essays

    data mining

    • 842 Words
    • 4 Pages

    Introduction to Data Mining Assignment 1 Ex1.1 what is data mining? (a) Is it another hype? Data mining is Knowledge extraction from data this need for data mining has arisen due to the wide availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge. So, data mining definitely is not another hype it can be viewed as the result of the natural evolution of information technology. (b) Is it a simple transformation of technology developed…

    • 842 Words
    • 4 Pages
    Good Essays
  • Good Essays

    Data Mining

    • 1660 Words
    • 7 Pages

    Data Mining: What is Data Mining? Overview Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified…

    • 1660 Words
    • 7 Pages
    Good Essays
  • Good Essays

    Data Mining

    • 782 Words
    • 4 Pages

    Data Mining Abdullah Alshawdhabi Coleman University Simply stated data mining refers to extracting or mining knowledge from large amounts of it. The term is actually a misnomer. Remember that the mining of gold from rocks or sand is referred to as gold mining rather than rock or sand mining. Thus, data mining should have been more appropriately named “knowledge mining from data,” which is unfortunately somewhat long. Knowledge mining, a shorter term, may not…

    • 782 Words
    • 4 Pages
    Good Essays
  • Good Essays

    Data Mining

    • 989 Words
    • 4 Pages

    Data Mining On Medical Domain Smita Malik, Karishma Naik, Archa Ghodge, Shivani Gaunker Shree Rayeshwar Institute of Engineering & Information Technology Shiroda, Goa, India. Smilemalik777@gmail.com; naikkarishma39@gmail.com; archaghodge@gmail.com; shivanigaunker@gmail.com Abstract-The successful application of data mining in highly visible fields like retail, marketing & e-business have led to the popularity of its use in knowledge discovery in databases (KDD) in other industries…

    • 989 Words
    • 4 Pages
    Good Essays