Abstract
The complexity of contemporary data warrants a need for better analysing tools in investigative areas. Human processing of data is no longer a viable option. We present an architecture of a novel universal system for analysis of graph-structured data, where data-mining and similarity-search operators can be used to discover or search for unknown information. We also present results that were obtained by our prototype implementation on two real-world data collections: the Twitter Higg’s boson dataset and the Kosarak dataset.
Supervised by P. Zezula.
This work has been supported by the Ministry of the Interior of the Czech Republic under the “Security Research for the Needs of the State Program 2015–2020,” through the Project No. VI20172020096, “Complex Analysis and Visualization of Large-scale Heterogeneous Data.”
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References
Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings of the 20th International Conference on Very Large Data Bases, VLDB, vol. 1215, pp. 487–499 (1994)
Aryabarzan, N., Minaei-Bidgoli, B., Teshnehlab, M.: negFIN: an efficient algorithm for fast mining frequent itemsets. Expert Syst. Appl. 105, 129–143 (2018)
Batko, M., Novak, D., Zezula, P.: MESSIF: metric similarity search implementation framework. In: Thanos, C., Borri, F., Candela, L. (eds.) DELOS 2007. LNCS, vol. 4877, pp. 1–10. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-77088-6_1
Bodon, F.: A fast apriori implementation. In: FIMI, vol. 3, p. 63 (2003)
De Domenico, M., Lima, A., Mougel, P., Musolesi, M.: The anatomy of a scientific rumor. Sci. Rep. 3, 2980 (2013)
Fournier-Viger, P., et al.: The SPMF open-source data mining library version 2. In: Berendt, B., et al. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9853, pp. 36–40. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46131-1_8
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: ACM Sigmod Record, vol. 29, pp. 1–12. ACM (2000)
Leskovec, J., et al.: Stanford network analysis project (2010). http://snap.stanford.edu
Srikant, R., Agrawal, R.: Mining sequential patterns: Generalizations and performance improvements. In: Apers, P., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 1–17. Springer, Heidelberg (1996). https://doi.org/10.1007/BFb0014140
Zezula, P., Amato, G., Dohnal, V., Batko, M.: Similarity Search: The Metric Space Approach, vol. 32. Springer, Boston (2006). https://doi.org/10.1007/0-387-29151-2
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Peschel, J., Zezula, P. (2019). ADAMiSS: Advanced Data Analysis, Mining and Search, System. In: Amato, G., Gennaro, C., Oria, V., Radovanović , M. (eds) Similarity Search and Applications. SISAP 2019. Lecture Notes in Computer Science(), vol 11807. Springer, Cham. https://doi.org/10.1007/978-3-030-32047-8_31
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DOI: https://doi.org/10.1007/978-3-030-32047-8_31
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