SECS: Concordance and Similarity of SEquences

Introduction

SECS is written in Python and has the following features:

• Concordance measure and pattern extractions from a set of preference ordering sequences

• Sequences kernel functions

Citations: please cite the following publications if you like to use the package.

[1]Zhiwei Lin, Yi Li, Xiaolian Guo: Consensus of rankings. CoRR abs/1704.08464 (2017), https://arxiv.org/abs/1704.08464

[2]Zhiwei Lin, Hui Wang, Cees H. Elzinga. Concordance and the Smallest Covering Set of Preference Orderings. http://arxiv.org/abs/1609.04722

[3]Cees Elzinga, Hui Wang, Zhiwei Lin, Yash Kumar, Concordance and consensus, Information Sciences, Volume 181, Issue 12, 15 June 2011, Pages 2529-2549, ISSN 0020-0255, http://dx.doi.org/10.1016/j.ins.2011.02.001.

[4]Cees H. Elzinga, Hui Wang, Versatile string kernels, Theoretical Computer Science, Volume 495, 2013, Pages 50-65, ISSN 0304-3975, http://dx.doi.org/10.1016/j.tcs.2013.06.006.

[5]Cees Elzinga, Sven Rahmann, Hui Wang, Algorithms for subsequence combinatorics, Theoretical Computer Science, Volume 409, Issue 3, 2008, Pages 394-404, ISSN 0304-3975, http://dx.doi.org/10.1016/j.tcs.2008.08.035.

Reporting bugs:

Please contact Dr. Zhiwei Lin at [http://scm.ulster.ac.uk/zhiwei.lin/]