Software

Iterative Label Spreading

Commonwealth Scientific and Industrial Research Organisation
Barnard, Amanda ; Parker, Amanda
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25919/5d806280b91a9&rft.title=Iterative Label Spreading&rft.identifier=https://doi.org/10.25919/5d806280b91a9&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=Based on a general definition of a cluster and the quality of a clustering result, this code presents a new method for evaluating existing clustering algorithms, or undertaking clustering, capable of predicting the number and type of clusters and outliers present in a data set, regardless of the complexity of the distribution of points. This algorithm, referred to as iterative label spreading (ILS), can recognize the characteristics expected of a successful clustering result before any clustering algorithm has been applied, providing a type of hyper-parameter optimization for clustering. In this notebook the algorithm, is assessed using large benchmark two-dimensional synthetic data sets, with tutorial examples. &rft.creator=Barnard, Amanda &rft.creator=Parker, Amanda &rft.date=2019&rft.edition=v1&rft_rights=CSIRO Open Source Software Licence (Based on MIT/BSD Open Source Licence) https://research.csiro.au/dap/licences/csiro-open-source-software-licence-based-on-mit-bsd-open-source-licence/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO 2019.&rft_subject=machine learning&rft_subject=clustering&rft_subject=classification&rft_subject=materials informatics&rft_subject=Artificial intelligence not elsewhere classified&rft_subject=Artificial intelligence&rft_subject=INFORMATION AND COMPUTING SCIENCES&rft.type=Computer Program&rft.language=English Access the software
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CSIRO Open Source Software Licence (Based on MIT/BSD Open Source Licence)
https://research.csiro.au/dap/licences/csiro-open-source-software-licence-based-on-mit-bsd-open-source-licence/

Data is accessible online and may be reused in accordance with licence conditions

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Based on a general definition of a cluster and the quality of a clustering result, this code presents a new method for evaluating existing clustering algorithms, or undertaking clustering, capable of predicting the number and type of clusters and outliers present in a data set, regardless of the complexity of the distribution of points. This algorithm, referred to as iterative label spreading (ILS), can recognize the characteristics expected of a successful clustering result before any clustering algorithm has been applied, providing a type of hyper-parameter optimization for clustering. In this notebook the algorithm, is assessed using large benchmark two-dimensional synthetic data sets, with tutorial examples.

Available: 2019-09-17

Data time period: 2019-01-01 to ..

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Subjects
Artificial Intelligence | Artificial Intelligence Not Elsewhere Classified | Information and Computing Sciences | classification | clustering | machine learning | materials informatics |

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Identifiers
ACN 633 798 857