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- W4297663721 abstract "In many real life problems, objects are described by large number of binary features. For instance, documents are characterized by presence or absence of certain keywords; cancer patients are characterized by presence or absence of certain mutations etc. In such cases, grouping together similar objects/profiles based on such high dimensional binary features is desirable, but challenging. Here, I present a Bayesian non parametric algorithm for clustering high dimensional binary data. It uses a Dirichlet Process (DP) mixture model and simulated annealing to not only cluster binary data, but also find optimal number of clusters in the data. The performance of the algorithm was evaluated and compared with other algorithms using simulated datasets. It outperformed all other clustering methods that were tested in the simulation studies. It was also used to cluster real datasets arising from document analysis, handwritten image analysis and cancer research. It successfully divided a set of documents based on their topics, hand written images based on different styles of writing digits and identified tissue and mutation specificity of chemotherapy treatments." @default.
- W4297663721 created "2022-09-30" @default.
- W4297663721 creator A5026673248 @default.
- W4297663721 date "2016-03-08" @default.
- W4297663721 modified "2023-09-25" @default.
- W4297663721 title "A Bayesian non-parametric method for clustering high-dimensional binary data" @default.
- W4297663721 doi "https://doi.org/10.48550/arxiv.1603.02494" @default.
- W4297663721 hasPublicationYear "2016" @default.
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