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- W1495677056 abstract "This chapter introduces a recent clustering framework, called splitting-merging awareness tactics (SMART). SMART integrates many crucial clustering techniques such as cluster-splitting methods, cluster-similarity measurement and clustering selection, within a framework to mimic human perception doing the sorting and grouping. The framework starts with one or two clusters and accomplishes many clustering tasks to split and merge clusters. The chapter also introduces related work, including self-splitting competitive learning (SSCL), self-splitting-merging competitive learning (SSMCL), unsupervised learning of finite mixture models (ULFMM), variational Bayesian Gaussian model (VBGM), parameter-free clustering (PFClust) and density-based spatial clustering of applications with noise (DBSCAN). The chapter describes the philosophy of the SMART framework. Subsequently, it discusses the clustering techniques employed in the SMART framework. The chapter also describes the enhanced SMART (E-SMART) structure and presents some examples of the SMART framework." @default.
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- W1495677056 date "2015-03-20" @default.
- W1495677056 modified "2023-10-16" @default.
- W1495677056 title "Splitting‐Merging Awareness Tactics (SMART)" @default.
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- W1495677056 doi "https://doi.org/10.1002/9781118906545.ch23" @default.
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