Metadata-Version: 2.1
Name: ots-eval
Version: 0.0.4
Summary: Over-Time Stability Evaluation
Home-page: https://github.com/tatusch/ots-eval
Author: Martha Krakowski, Gerhard Klassen
Author-email: martha.krakowski@hhu.de, gerhard.klassen@hhu.de
License: UNKNOWN
Project-URL: Source Code, https://github.com/tatusch/ots-eval
Description: # Over-Time Stability Evaluation
        **ots-eval** is a toolset for the over-time stability evaluation of multiple multivariate time series based on cluster transitions. It contains an over-time stability measure for crisp over-time clusterings called _CLOSE_ [[1]](#1), one stability measure for fuzzy over-time clusterings called _FCSETS_ [[2]](#2), two outlier detection algorithms _DOOTS_ [[3](#3),[4](#4)] and _DACT_ [[5]](#5) addressing cluster-transition-based outliers and an over-time clustering algorithm named _C(OTS)^2_ [[6]](#6).
        All approaches focus on multivariate time series data that is clustered per timestamp.
        
        The toolset was implemented by [Martha Krakowski (Tatusch)](https://dbs.cs.uni-duesseldorf.de/mitarbeiter.php?id=tatusch) and [Gerhard Klassen](https://dbs.cs.uni-duesseldorf.de/mitarbeiter.php?id=klassen).
        ## Installation
        You can simply install ots-eval by using pip:
        ```shell script
        pip install ots-eval
        ```
        You can import the package in your Python script via:
        ```python
        import ots_eval
        ```
        ### Dependencies
        ots-eval requires:
        * python>=3.7
        * pandas>=1.0.0
        * numpy>=1.19.2
        * scipy>=1.3.0
        
        ## Documentation
        In the `doc` folder, there are some explanations for the usage of every approach. 
        * [CLOSE](https://github.com/tatusch/ots-eval/blob/main/doc/close.md)
        * [FCSETS](https://github.com/tatusch/ots-eval/blob/main/doc/fcsets.md)
        * [DOOTS](https://github.com/tatusch/ots-eval/blob/main/doc/doots.md)
        * [DACT](https://github.com/tatusch/ots-eval/blob/main/doc/dact.md)
        * [C(OTS)^2](https://github.com/tatusch/ots-eval/blob/main/doc/cots.md)
        
        ## License
        ots-eval is distributed under the 3-Clause BSD license.
        
        ## References
        This toolset is the implementation of approaches from our following works:
        
        <a id="1">[[1]](http://www.ibai-publishing.org/html/proceedings_2020/pdf/proceedings_book_MLDM_2020.pdf)</a>
        Tatusch, M., Klassen, G., Bravidor, M., and Conrad, S. (2020).  
        **How is Your Team Spirit? Cluster Over-Time Stability Evaluation**.  
        In: _Machine Learning and Data Mining in Pattern Recognition, 16th International Conference on Machine Learning and
        Data Mining, MLDM 2020_, pages 155–170.
        
        <a id="2">[[2]](https://link.springer.com/chapter/10.1007%2F978-3-030-50146-4_50)</a>
        Klassen, G., Tatusch, M., Himmelspach, L., and Conrad, S. (2020).  
        **Fuzzy Clustering Stability Evaluation of Time Series**.  
        In: _Information Processing and Management of Uncertainty in Knowledge-Based Systems, 18th International Conference, IPMU 2020_, pages 680-692.
        
        <a id="3">[[3]](https://link.springer.com/chapter/10.1007/978-981-15-1699-3_8)</a>
        Tatusch, M., Klassen, G., Bravidor, M., and Conrad, S. (2019).  
        **Show me your friends and i’ll tell you who you are. Finding anomalous time series by conspicuous clus-
        ter transitions**.  
        In: _Data Mining. AusDM 2019. Communications in Computer and Information Science_, pages 91–103.
        
        <a id="4">[[4]](https://link.springer.com/chapter/10.1007/978-3-030-59065-9_26)</a>
        Tatusch, M., Klassen, G., and Conrad, S. (2020).  
        **Behave or be detected! Identifying outlier sequences by their group cohesion**.  
        In: _Big Data Analytics and KnowledgeDiscovery, 22nd International Conference, DaWaK 2020_, pages 333–347.
        
        <a id="5">[[5]](https://link.springer.com/chapter/10.1007%2F978-3-030-65390-3_28)</a>
        Tatusch, M., Klassen, G., and Conrad, S. (2020).  
        **Loners stand out. Identification of anomalous subsequences based on group performance**.  
        In: _Advanced Data Mining and Applications, ADMA 2020_, pages 360–369.
        
        <a id="6">[[6]](https://ieeexplore.ieee.org/document/9308516) 
        Klassen, G., Tatusch, M., and Conrad, S. (2020).  
        **Clustering of time series regarding their over-time stability**.  
        In: _Proceedings of the 2020 IEEE Symposium Series on Computational Intelligence (SSCI)_, pages 1051–1058.
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: BSD License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
