Abstract
The increase in new electronic devices had generated a considerable increase in obtaining spatial data information; hence these data are becoming more and more widely used. As well as for conventional data, spatial data need to be analyzed so interesting information can be retrieved from them. Therefore, data clustering techniques can be used to extract clusters of a set of spatial data. However, current approaches do not consider the implicit semantics that exist between a region and an object’s attributes. This paper presents an approach that enhances spatial data mining process, so they can use the semantic that exists within a region. A framework was developed, OntoSDM, which enables spatial data mining algorithms to communicate with ontologies in order to enhance the algorithm’s result. The experiments demonstrated a semantically improved result, generating more interesting clusters, therefore reducing manual analysis work of an expert.
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References
Arvor, D., Durieux, L., Andrés, S., Laporte, M.-A.: Advances in Geographic Object-Based Image Analysis with ontologies: A review of main contributions and limitations from a remote sensing perspective. ISPRS J. Photogramm. Remote Sens. 82, 125–137 (2013)
Mennis, J., Guo, D.: Spatial data mining and geographic knowledge discovery—An introduction. Comput. Environ. Urban Syst. 33, 403–408 (2009)
Yueshun, H.: A study of spatial data mining architecture and technology. In: 2009 2nd IEEE Int. Conf. Comput. Sci. Inf. Technol., pp. 163–166 (2009)
Ji, M., Jin, F., Zhao, X., Ai, B., Li, T.: Mine geological hazard multi-dimensional spatial data warehouse construction research. In: 18th International Conference on Geoinformatics, pp. 1–5. IEEE (2010)
Hongfei, C., Xiaoyan, W.: Research on GIS-based spatial data mining. In: 2011 Int. Conf. Bus. Manag. Electron. Inf., pp. 351–354 (2011)
Han, J., Kamber, M.: Data mining: concepts and techniques. Morgan Kaufmann (2011)
Pattabiraman, V., et al.: A Novel Spatial clustering with Obstacles and Facilitators Co-straint Based on Edge Deduction and K- Mediods. In: Proceeding of 2009 International Conference on Computer Technology and Development, vol. 1, pp. 402–406. IEEE Computer Society, Kota Kinabalu (2009)
Bae, D.-H., Baek, J.-H., Oh, H.-K., Song, J.-W., Kim, S.-W.: SD-Miner: A spatial data mining system. In: 2009 IEEE Int. Conf. Netw. Infrastruct. Digit. Content, pp. 803–807 (2009)
Han, J., Kamber, M., Tung, A.K.H.: Spatial Clustering Methods in Data Mining: A Survey. Geogr. Data Min. Knowl. Discov. 1–29 (2001)
MacQueen, J.: Some Methods for Classification and Analysis of Multivariate Observations. In: Proc. fifth Berkeley Symp..., vol. 233, pp. 281–297 (1967)
Kaufman, L., Rousseeuw, P.: Finding groups in data: an introduction to cluster analysis (1990)
Karypis, G., Han, E.H., Kumar, V.: CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling. IEEE Comput (1999)
Ester, M., Kriegel, H., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: KDD (1996)
Ankerst, M., Breunig, M.M., Kriegel, H.-P., Sander, J.: OPTICS: Ordering Points to Identify the Clustering Structure. In: Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, pp. 49–60. ACM, New York (1999)
Ongenae, F., Claeys, M., Dupont, T., Kerckhove, W., Verhoeve, P., Dhaene, T., De Turck, F.: A probabilistic ontology-based platform for self-learning context-aware healthcare applications. Expert Syst. Appl. 40, 7629–7646 (2013)
Ayres, R., Santos, M.P.: FOntGAR algorithm: Mining generalized association rules using fuzzy ontologies. Fuzzy Syst (FUZZ-IEEE),... 10–15 (2012)
Chandrasekaran, B., Josephson, J.R., Benjamins, V.R.: What are ontologies, and why do we need them? IEEE Intell. Syst. 14, 20–26 (1999)
Liao, S.-H., Chen, J.-L., Hsu, T.-Y.: Ontology-based data mining approach implemented for sport marketing. Expert Syst. Appl. 36, 11045–11056 (2009)
Allemang, D., Hendler, J.: Semantic web for the working ontologist: effective modeling in RDFS and OWL. Elsevier (2011)
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Valêncio, C.R., Guimarães, D.L., Zafalon, G.F.D., Neves, L.A., Colombini, A.C. (2015). OntoSDM: An Approach to Improve Quality on Spatial Data Mining Algorithms. In: Italiano, G.F., Margaria-Steffen, T., Pokorný, J., Quisquater, JJ., Wattenhofer, R. (eds) SOFSEM 2015: Theory and Practice of Computer Science. SOFSEM 2015. Lecture Notes in Computer Science, vol 8939. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46078-8_46
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DOI: https://doi.org/10.1007/978-3-662-46078-8_46
Publisher Name: Springer, Berlin, Heidelberg
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