MiniPalindrome

Paaßen B (2016) : Bielefeld University. doi:10.4119/unibi/2900666.

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Abstract / Bemerkung
This is a dataset of 48 Java computer programs, all solving the same programming task, namely detecting whether all words in the input string are palindromes. The programs have been created by myself in 2012 as part of the DFG funded project _Learning Feedback for Dynamic Tutoring Systems_ (FIT) with grant number HA 2719/6-1. It is meant as a benchmark dataset for methods working on clustering and/or classification of structured data (sequences, trees or graphs). We provide the underlying raw data, as well as an intermediate graph representation and pre-calculated distance matrices.
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This MiniPalindrome is made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0. Any rights in individual contents of the database are licensed under the Database Contents License: http://opendatacommons.org/licenses/dbcl/1.0/
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Paaßen B. (2016): MiniPalindrome. Bielefeld University. doi:10.4119/unibi/2900666.
Paaßen, B. (2016). MiniPalindrome. Bielefeld University. doi:10.4119/unibi/2900666
Paaßen, B. (2016). MiniPalindrome. Bielefeld University. doi:10.4119/unibi/2900666.
Paaßen, B., 2016. MiniPalindrome. Bielefeld University. doi:10.4119/unibi/2900666
B. Paaßen, MiniPalindrome. Bielefeld University, 2016. doi:10.4119/unibi/2900666.
Paaßen, B.: MiniPalindrome. Bielefeld University (2016). doi:10.4119/unibi/2900666.
Paaßen, Benjamin. MiniPalindrome. Bielefeld University, 2016. doi:10.4119/unibi/2900666
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2017-10-05T08:29:09Z

Material in PUB:
forms.relation..display
Execution Traces as a Powerful Data Representation for Intelligent Tutoring Systems for Programming
Paaßen B, Jensen J, Hammer B (2016)
In: Proceedings of the 9th International Conference on Educational Data Mining. Barnes T, Chi M, Feng M (Eds); Raleigh, North Carolina, USA: International Educational Datamining Society: 183-190.
forms.relation..display
Gaussian process prediction for time series of structured data
Paaßen B, Göpfert C, Hammer B (2016)
In: Proceedings of the ESANN, 24th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); Louvain-la-Neuve: Ciaco - i6doc.com: 41--46.

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