Multi-level conformal clustering: A distribution-free technique for clustering and anomaly detection
| Autor: | Ilia Nouretdinov, Matteo Fontana, Daljit Rehal, James Gammerman |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Distribution free
FOS: Computer and information sciences 0209 industrial biotechnology Computer Science - Machine Learning Computer science Cognitive Neuroscience Conformal map Machine Learning (stat.ML) 02 engineering and technology Clustering Machine Learning (cs.LG) Methodology (stat.ME) 020901 industrial engineering & automation Artificial Intelligence Robustness (computer science) Statistics - Machine Learning 0202 electrical engineering electronic engineering information engineering Cluster analysis Statistics - Methodology Dendrograms business.industry Dendrogram Pattern recognition Conformal prediction Computer Science Applications Hierarchical clustering 020201 artificial intelligence & image processing Anomaly detection Artificial intelligence business |
| Popis: | In this work we present a clustering technique called multi-level conformal clustering (MLCC). The technique is hierarchical in nature because it can be performed at multiple significance levels which yields greater insight into the data than performing it at just one level. We describe the theoretical underpinnings of MLCC, compare and contrast it with the hierarchical clustering algorithm, and then apply it to real world datasets to assess its performance. There are several advantages to using MLCC over more classical clustering techniques: Once a significance level has been set, MLCC is able to automatically select the number of clusters. Furthermore, thanks to the conformal prediction framework the resulting clustering model has a clear statistical meaning without any assumptions about the distribution of the data. This statistical robustness also allows us to perform clustering and anomaly detection simultaneously. Moreover, due to the flexibility of the conformal prediction framework, our algorithm can be used on top of many other machine learning algorithms. |
| Databáze: | OpenAIRE |
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