Abstract
We provide a general framework for learning characterization rules of a set of objects in Geographic Information Systems (GIS) relying on the definition of distance quantified paths. Such expressions specify how to navigate between the different layers of the GIS starting from the target set of objects to characterize. We have defined a generality relation between quantified paths and proved that it is monotonous with respect to the notion of coverage, thus allowing to develop an interactive and effective algorithm to explore the search space of possible rules. We describe GISMiner, an interactive system that we have developed based on our framework. Finally, we present our experimental results from a real GIS about mineral exploration.
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References
Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Proc. of the ACM SIGMOD International Conference on Management of Data, pp. 207–213 (1993)
Appice, A., Ceci, M., Lanza, A., Lisi, F.A., Malerba, D.: Discovery of spatial association rules in geo-referenced census data: A relational mining approach. Intell. Data Anal. 7(6), 541–566 (2003)
Appice, A., Ciampi, A., Lanza, A., Malerba, D., Rapolla, A., Vetturi, L.: Geographic knowledge discovery in INGENS: an inductive database perspective. In: ICDM Workshops, pp. 326–331 (2008)
Bay, S.D., Pazzani, M.J.: Detecting group differences: Mining contrast sets. Data Min. Knowl. Discov. 5(3), 213–246 (2001)
Cassard, D.: GIS andes: a metallogenic GIS of the andes cordillera. In: 4th Int. Symp. on Andean Geodynamics, pp. 147–150. IRD Paris, October 1999
Dehaspe, L., De Raedt, L.: Mining association rules in multiple relations. In: Džeroski, S., Lavrač, N. (eds.) ILP 1997. LNCS, vol. 1297, pp. 125–132. Springer, Heidelberg (1997)
Dong, G., Li, J.: Efficient mining of emerging patterns: discovering trends and differences. In: KDD, pp. 43–52 (1999)
Fan, H.: Efficiently mining interesting emerging patterns. In: Dong, G., Tang, C., Wang, W. (eds.) WAIM 2003. LNCS, vol. 2762, pp. 189–201. Springer, Heidelberg (2003)
Furnkranz, J.: Separate-and-conquer rule learning. Technical Report OEFAI-TR-96-25, Austrian Research Institute for Artificial Intelligence Schottengasse (1996)
Gouda, K., Zaki, M.J.: Efficiently mining maximal frequent itemsets. In: 1st IEEE International Conference on Data Mining, November 2001
Han, J., Koperski, K., Stefanovic, N.: Geominer: a system prototype for spatial data mining. In: SIGMOD 1997: Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, pp. 553–556. ACM, New York (1997)
Klösgen, W.: Explora: a multipattern and multistrategy discovery assistant. In: Advances in Knowledge Discovery and Data Mining, pp. 249–271 (1996)
Klösgen, W., May, M.J.: Spatial subgroup mining integrated in an object-relational spatial database. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) PKDD 2002. LNCS (LNAI), vol. 2431, pp. 275–286. Springer, Heidelberg (2002)
Koperski, K., Han, J.: Discovery of spatial association rules in geographic information databases. In: Egenhofer, M., Herring, J.R. (eds.) SSD 1995. LNCS, vol. 951, pp. 47–66. Springer, Heidelberg (1995)
Koperski, K., Han, J., Stefanovic, N.: An efficient two-step method for classification of spatial data. In: Proc. International Symposium on Spatial Data Handling SDH 1998, pp. 45–54 (1998)
Lavrač, N., Flach, P.A., Zupan, B.: Rule evaluation measures: a unifying view. In: Džeroski, S., Flach, P.A. (eds.) ILP 1999. LNCS (LNAI), vol. 1634, pp. 174–185. Springer, Heidelberg (1999)
Malerba, D., Esposito, F., Lanza, A., Lisi, F.A.: Discovering geographic knowledge: the INGENS system. In: Ohsuga, S., Raś, Z.W. (eds.) ISMIS 2000. LNCS (LNAI), vol. 1932, pp. 40–48. Springer, Heidelberg (2000)
Malerba, D., Esposito, F., Lanza, A., Lisi, F.A., Appice, A.: Empowering a GIS with inductive learning capabilities: the case of INGENS. Computers, Environment and Urban Systems 27(3), 265–281 (2003)
Mannila, H., Toivonen, H.: Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery 1(3), 241–258 (1997)
Miller, H.J., Han, J.: Geographic Data Mining and Knowledge Discovery. Taylor & Francis Inc., Bristol (2001)
Raymond, T.Ng., Han, J.: Efficient and effective clustering methods for spatial data mining. In: VLDB 1994: Proceedings of the 20th International Conference on Very Large Data Bases, pp. 144–155. Morgan Kaufmann Publishers Inc., San Francisco (1994)
Nijssen, S., Kok, J.N.: Efficient frequent query discovery in Farmer. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 350–362. Springer, Heidelberg (2003)
Novak, P.K., Lavrac, N., Webb, G.I.: Supervised descriptive rule discovery: A unifying survey of contrast set, emerging pattern and subgroup mining. Journal of Machine Learning Research 10, 377–403 (2009)
Salleb, A., Vrain, C.: An application of association rules discovery to geographic information systems. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 613–618. Springer, Heidelberg (2000)
Turmeaux, T., Salleb, A., Vrain, C., Cassard, D.: Learning characteristic rules relying on quantified paths. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 471–482. Springer, Heidelberg (2003)
Wrobel, S.: An algorithm for multi-relational discovery of subgroups. In: Komorowski, J., Żytkow, J.M. (eds.) PKDD 1997. LNCS, vol. 1263, pp. 78–87. Springer, Heidelberg (1997)
Zelezný, F., Lavrac, N.: Propositionalization-based relational subgroup discovery with rsd. Machine Learning 62(1–2), 33–63 (2006)
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Salleb-Aouissi, A., Vrain, C., Cassard, D. (2015). Learning Characteristic Rules in Geographic Information Systems. In: Bassiliades, N., Gottlob, G., Sadri, F., Paschke, A., Roman, D. (eds) Rule Technologies: Foundations, Tools, and Applications. RuleML 2015. Lecture Notes in Computer Science(), vol 9202. Springer, Cham. https://doi.org/10.1007/978-3-319-21542-6_28
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