SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Iago Breno Araujo(ibacaraujo@ecomp.uefs.br)
2 Rodrigo Calumby(rtcalumby@ecomp.uefs.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 Boteanu, B., Mironica, I., and Ionescu, B. (2015). Hierarchical clustering pseudo-relevance feedback for social image search result diversification. In CBMI, pages 1–6.
2 Calumby, R. T., Araujo, I. B. A. d. C., Santana, V. P., Munoz, J. A., Penatti, O. A., Li, L. T., Almeida, J., Chiachia, G., Gonçalves, M. A., and Torres, R. d. S. (2015). Recod @ mediaeval 2015: Diverse social images retrieval. Working Notes of MediaEval.
3 Gan, G., Ma, C., and Wu, J. (2007). Data clustering: theory, algorithms, and applications, volume 20. Siam.
4 Han, J., Kamber, M., and Pei, J. (2012). Data Mining: Concepts and techniques. Morgan Kaufmann.
5 Ionescu, B., Popescu, A., Lupu, M., Gînsca, A.-L., and Müller, Henning, B. B. (2015). Retrieving diverse social images at mediaeval 2015: Challenge, dataset and evaluation. In MediaEval.
6 Jain, A. K. (2010). Data clustering: 50 years beyond k-means. Pattern Recogn. Lett., 31(8):651–666.
7 Karypis, G., Han, E.-H., and Kumar, V. (1999). Chameleon: Hierarchical clustering using dynamic modeling. Computer, 32(8):68–75.
8 Liang, S., Ren, Z., and De Rijke, M. (2014). Fusion helps diversification. In ACM SIGIR, pages 303–312. ACM.
9 Ounis, I., Macdonald, C., and Santos, R. L. (2015). Search result diversification. Found Trends Inf Ret, 9(1):1–90.
10 Sabetghadam, S., Palotti, J., Rekabsaz, N., Lupu, M., and Hanburry, A. (2015). Tuw @ mediaeval 2015 retrieving diverse social images. Working Notes of MediaEval.
11 Spyromitros-Xioufis, E., Popescu, A., Papadopoulos, S., and Kompatsiaris, I. (2015). Usemp: Finding diverse images at mediaeval 2015. Working Notes of MediaEval.
12 Strehl, A. and Ghosh, J. (2002). Cluster ensembles—a knowledge reuse framework for combining multiple partitions. JMLR, 3(Dec):583–617.
13 Veltkamp, R. C. and Tanase, M. (2002). Content-Based Image and Video Retrieval, chapter A Survey of Content-Based Image Retrieval Systems, pages 47–101.
14 Zaki, M. J. and Meira Jr, W. (2014). Data Mining and Analysis: Fundamental Concepts and Algorithms. Cambridge University Press, New York, NY.
15 Zhang, T., Ramakrishnan, R., and Livny, M. (1996). Birch: an efficient data clustering method for very large databases. In ACM SIGMOD, volume 25, pages 103–114.