Abstract
We introduce meta-pattern discovery from a data ensemble, a new paradigm of pattern discovery which goes beyond the KDD process model. A data ensemble, which represents a set of data sets, seems to be more natural as a model of the big data (We focus on the volume and velocity aspects of the big data.). We propose two kinds of meta-patterns, each of which specifies patterns such as clusters for a set of data sets, for an unsupervised setting and a supervised one. Our solutions for these settings were shown to be feasible with one synthetic and two real data ensembles by experiments.
Keywords
E. Suzuki—A part of this research was supported by Grant-in-Aid for Scientific Research 25280085 and 15K12100 from the Japanese Ministry of Education, Culture, Sports, Science and Technology.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
In this paper we use the same values and denote them \(\beta \).
- 2.
We adopt the standard procedure of using the Laplace correction.
- 3.
This condition is our modification to the original BIRCH.
- 4.
Note that this solution is independent from the one in the previous section. A semi-supervised, hybrid solution is beyond the scope of this paper.
- 5.
We use the past tense for our past actions.
- 6.
Preliminary experiments showed that the number of the random restart has a minor influence to the performance as long as it is not extremely small.
- 7.
Due to the good performance under these conditions, we believe that our method outperforms sampling-based k-means algorithms as well as state-of-the-art methods.
- 8.
As feasibility study, we did not compare our method with other methods.
- 9.
We used the default setting of ELLA http://www.seas.upenn.edu/~eeaton/publications.html.
References
Aggarwal, C.C., Han, J., Wang, J., Yu, P.S.: A framework for clustering evolving data streams. In: Proceedings of VLDB 2003, pp. 81–92 (2003)
DuMouchel, W., Volinsky, C., Johnson, T., Cortes, C., Pregibon, D.: Squashing flat files flatter. In: Proceedings of KDD 1999, pp. 6–15 (1999)
Erna, A., Yu, L., Zhao, K., Chen, W., Suzuki, E.: Facial expression data constructed with Kinect and their clustering stability. In: Ślȩzak, D., Schaefer, G., Vuong, S.T., Kim, Y.-S. (eds.) AMT 2014. LNCS, vol. 8610, pp. 421–431. Springer, Heidelberg (2014)
Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery: an overview. In: Advances in Knowledge Discovery and Data Mining, pp. 1–34. AAAI/MIT Press, Menlo Park (1996)
Feldman, D., Schmidt, M., Sohler, C.: Turning big data into tiny data: constant-size coresets for \(k\)-means, PCA and projective clustering. In: Proceedings of SODA 2013, pp. 1434–1453 (2013)
Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2009)
Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)
Ruvolo, P., Eaton, E.: ELLA: an efficient lifelong learning algorithm. In: Proceedings of ICML, vol. 1, pp. 507–515 (2013)
Seidl, T., Assent, I., Kranen, P., Krieger, R., Herrmann, J.: Indexing density models for incremental learning and anytime classification on data streams. In: Proceedings of EDBT 2009, pp. 311–322 (2009)
Zhang, D., Zhou, Z.-H., Chen, S.: Semi-supervised dimensionality reduction. In: Proceedings of SDM 2007, pp. 629–634 (2007)
Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: a new data clustering algorithm and its applications. Data Min. Knowl. Discov. 1(2), 141–182 (1997)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Suzuki, E. (2015). On the Feasibility of Discovering Meta-Patterns from a Data Ensemble. In: Japkowicz, N., Matwin, S. (eds) Discovery Science. DS 2015. Lecture Notes in Computer Science(), vol 9356. Springer, Cham. https://doi.org/10.1007/978-3-319-24282-8_22
Download citation
DOI: https://doi.org/10.1007/978-3-319-24282-8_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24281-1
Online ISBN: 978-3-319-24282-8
eBook Packages: Computer ScienceComputer Science (R0)