"GeoPlot": spatial data mining on video libraries

ABSTRACT

Are "tornado" touchdowns related to "earthquakes"? How about to "floods", or to "hurricanes"? In Informedia [14], using a gazetteer on news video clips, we map news onto points on the globe and find correlations between sets of points. In this paper we show how to find answers to such questions, and how to look for patterns on the geo-spatial relationships of news events. The proposed tool is "GeoPlot", which is fast to compute and gives a lot of useful information which traditional text retrieval can not find.We describe our experiments on 2-year worth of video data (~ 20 Gbytes). There we found that GeoPlot can find unexpected correlations that text retrieval would never find, such as those between "earthquake" and "volcano", and "tourism" and "wine".In addition, GeoPlot provides a good visualization of a data set's characteristics. Characteristics at all scales are shown in one plot and a wealth of information is given, for example, geo-spatial clusters, characteristic scales, and intrinsic (fractal) dimensions of the events' locations.

REFERENCES

• 1: Rakesh Agrawal , Tomasz Imieliński , Arun Swami, Mining association rules between sets of items in large databases, Proceedings of the 1993 ACM SIGMOD international conference on Management of data, p.207-216, May 25-28, 1993, Washington, D.C., United States
• 2: Alberto Belussi , Christos Faloutsos, Estimating the Selectivity of Spatial Queries Using the `Correlation' Fractal Dimension, Proceedings of the 21th International Conference on Very Large Data Bases, p.299-310, September 11-15, 1995
• 3: N. A. C. Cressie. Statistics for Spatial Data. Wiley, 1991.
• 4: P. M. Dixon. Ripley's k function. Technical report 01-18, Department of Statistics, Iowa State University, December 2001.
• 5: Michalis Faloutsos , Petros Faloutsos , Christos Faloutsos, On power-law relationships of the Internet topology, Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication, p.251-262, August 30-September 03, 1999, Cambridge, Massachusetts, United States
• 6: C. T. Jr., A. Traina, L. Wu, and C. Faloutsos. Fast feature selection using the fractal dimension. XV Brazilian Symposium on Databases (SBBD), 2000.
• 7: K. Koperski, J. Adhikary, and J. Han. Spatial data mining: Progress and challenges. Proc. of SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, 1996.
• 8: W. Lu, J. Han, and B. C. Ooi. Discovery of general knowledge in large spatial databases. Proc. of Fareast Workshop on Geographic Information Systems, 1993.
• 9: Thomas M. Mitchell, Machine Learning, McGraw-Hill Higher Education, 1997
• 10: M. Schroeder. Fractals, Chaos, Power Laws: Minutes From an Infinite Paradise. W. H. Freeman and Company, 1991.
• 11: Amit Singhal , Chris Buckley , Mandar Mitra, Pivoted document length normalization, Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, p.21-29, August 18-22, 1996, Zurich, Switzerland [doi>10.1145/243199.243206]
• 12: Agma Traina , Caetano Traina , Spiros Papadimitriou , Christos Faloutsos, Tri-plots: scalable tools for multidimensional data mining, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, p.184-193, August 26-29, 2001, San Francisco, California [doi>10.1145/502512.502538]
• 13: E. M. Voorhees and D. K. Harman. Overview of the seventh Text REtrieval Conference (TREC-7). Proc. of the Seventh Text REtrieval Conference (TREC-7), 1999.
• 14: Howard D. Wactlar , Michael G. Christel , Yihong Gong , Alexander G. Hauptmann, Lessons Learned from Building a Terabyte Digital Video Library, Computer, v.32 n.2, p.66-73, February 1999 [doi>10.1109/2.745722]
• 15: G. K. Zipf. Human Behavior and Principle of Least Effort: an Introduction to Human Ecology. Addison Wesley, 1949.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Abstracting methods
      H.3.7 Digital Libraries
          Subjects: Collection

General Terms:
Algorithms

Keywords:
correlation integral, fractal dimension, intrinsic dimension, pair correlation, spatial data mining, video data mining