Java Sorting Programs

Paaßen B (2016)
Bielefeld University.

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Abstract
This is a dataset of 126 Java computer programs, all sorting an array of integers in ascending order. The programs have been gathered from the web in 2015 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). For copyright reasons, we can not provide the source code directly. Instead, this dataset contains meta data about the data points as well as pre-calculated distance matrices.
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This Java Sorting Programs 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. Java Sorting Programs. Bielefeld University; 2016.
Paaßen, B. (2016). Java Sorting Programs. Bielefeld University.
Paaßen, B. (2016). Java Sorting Programs. Bielefeld University.
Paaßen, B., 2016. Java Sorting Programs, Bielefeld University.
B. Paaßen, Java Sorting Programs, Bielefeld University, 2016.
Paaßen, B.: Java Sorting Programs. Bielefeld University (2016).
Paaßen, Benjamin. Java Sorting Programs. Bielefeld University, 2016.
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Sorting.zip 199.86 KB
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OA Open Access
Last Uploaded
2016-07-05T10:26:32Z
File Name
Sorting.zip 229.07 KB
Access Level
OA Open Access
Last Uploaded
2016-07-05T10:26:32Z

This data publication is cited in the following publications:
2710031
Metric learning for sequences in relational LVQ
Mokbel B, Paaßen B, Schleif F-M, Hammer B (2015)
Neurocomputing 169: 306-322.
2724156
Adaptive structure metrics for automated feedback provision in Java programming
Paaßen B, Mokbel B, Hammer B (2015)
In: Proceedings of the ESANN, 23rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Verleysen M (Ed); 307-312.
2783224
Adaptive structure metrics for automated feedback provision in intelligent tutoring systems
Paaßen B, Mokbel B, Hammer B (2016)
Neurocomputing 192(SI): 3-13.
2900676
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);Bruges: 41-46.
2904509
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.
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