ICube

University of Strasbourg

tomalampert@outlook.com TSCC contains constrained clustering implementations that use the DTW dissimilarity measure (although Euclidean is still an option) and, where necessary, DBA averaging. Please refer to the following publication for further details. ## Reference This toolbox accompanies the papers: T. Lampert, T.-B.-H. Dao, B. Lafabregue, N. Serrette, G. Forestier, B. Crémilleux, C. Vrain, P. Gançarski. Constrained Distance Based Clustering for Time-Series: A Comparative and Experimental Study. Data Mining and Knowledge Discovery, (accepted) and T. Lampert, B. Lafabregue, T.-B.-H. Dao, N. Serrette, C. Vrain, P. Gançarski. Constrained Distance Based Clustering for Satellite Image Time-Series. (submitted) ## Instructions To start using the toolbox, download some of the UCR datasets (http://www.cs.ucr.edu/~eamonn/time_series_data/) and edit the UCR_path in `prepare_Datasets.m` to point to the correct location. Using Matlab, execute `prepare_Datasets()`. This will create a subdirectory called datasets, which will contain the data in a form expected by the scripts (described in more detail below). It will convert all UCR datasets under the UCR_path. To execute these, use any of the `test_

**Name:** CCCG — Constrained Clustering using Column Generation

**Language:** C++

**URL:** https://dtai.cs.kuleuven.be/CP4IM/cccg/

**Publication:** B. Babaki, T. Guns, S. Nijjsen. Constrained Clustering using Column Generation. Proceedings of the 11th International Conference on Integration of Artificial Intelligence (AI) and Operations Research (OR) Techniques in Constraint Programming, 2014. * **Subdirectory:** BabakiXX

**Name:** MIPKmeans

**Language:** Python

**URL:** https://github.com/Behrouz-Babaki/MIPKmeans * **Subdirectory:** Cucuringu16

**Language:** Matlab

**Publication:** M. Cucuringu, I. Koutis, S. Chawla, G. Miller, R. Peng. Simple and Scalable Constrained Clustering: A Generalized Spectral Method. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016. * **Subdirectory:** Dao17

**Language:** C++ and Python

**Publication:** T.-B.-H. Dao, K.-C. Duong, C. Vrain. Constrained Clustering by Constraint Programming. Artificial Intelligence, 244, 70--94, 2017. * **Subdirectory:** Hiep16

**Name:** Pairwise Constrained Clustering By Local Search

**Language:** R

**URL:** https://github.com/cran/conclust/blob/master/R/ccls.R

**Publication:** T. K. Hiep, N. M. Duc, B. Q. Trung. Pairwise Constrained Clustering by Local Search. Proceedings of the 7th Symposium on Information and Communication Technology, 2016. * **Subdirectory:** Kamvar03

**Language:** Matlab

**Publication:** S. Kamvar, S. Klein, C. Manning. Spectral Learning. Proceedings of the 18th International Joint Conference on Artificial Intelligence, 2003. * **Subdirectory:** Li09

**Name:** CCSR — Constrained Clustering via Spectral Regularization

**Language:** Matlab

**URL:** http://www.ee.columbia.edu/~zgli/

**Publication:** Z. Li, J. Liu, X. Tang. Constrained Clustering via Spectral Regularization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2001. * **Subdirectory:** Wagstaff01

**Name:** COP-KMeans

**Language:** R

**URL:** https://github.com/cran/conclust/blob/master/R/ckmeans.R

**Publication:** K. Wagstaff, C. Cardie, S. Rogers, S. Schroedl. Constrained K-means Clustering with Background Knowledge. Proceedings of the 18th International Conference on Machine Learning, 2001. * **Subdirectory:** Wang14

**Name:** CSP

**Language:** Matlab

**URL:** https://github.com/gnaixgnaw/CSP

**Publication:** X. Wang, I. Davidson. Flexible Constrained Spectral Clustering. Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2010. ## Funding This work was carried out as part of the A2CNES project, which is funded by CNES/UNISTRA R&T grant number 2016-033.