کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
534591 870269 2013 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Using evolutionary algorithms for model-based clustering
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Using evolutionary algorithms for model-based clustering
چکیده انگلیسی

In mixture model-based clustering, parameter estimation is generally carried out using the expectation–maximization algorithm, or some closely related variant. We present a new approach by casting the model-fitting problem as a single-objective evolutionary algorithm that focuses on searching the cluster-membership space. The appeal of an evolutionary algorithm is its ability to more thoroughly search the parameter space, providing an approach inherently more robust with respect to local maxima. This approach is illustrated through application to both simulated and real clustering data sets where comparisons are drawn with traditional model-fitting algorithms.


► We introduce evolutionary algorithms as a robust alternative to the EM algorithm.
► The algorithms mutate the component indicator variables for model-based clustering.
► These algorithms perform favourably with respect to the log-likelihood.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 34, Issue 9, 1 July 2013, Pages 987–992
نویسندگان
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