کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
534950 870307 2008 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
An integer-coded evolutionary approach for mixture maximum likelihood clustering
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
An integer-coded evolutionary approach for mixture maximum likelihood clustering
چکیده انگلیسی

This paper outlines an algorithm for solving the maximum mixture likelihood clustering problem using an integer-coded genetic algorithm (IGA-ML) where a fixed length chromosome encodes the object-to-cluster assignment. The main advantage of the outlined algorithm (IGA-ML) compared with other known algorithms, such as the k-means technique, is that it can successfully discover the correct number of clusters, in addition to carrying out the partitioning process. The algorithm implements a post-fixing sorting mechanism that drastically reduces the searched solution space by eliminating duplicate solutions that appear after applying the genetic operations. Simulation results show the effectiveness of the algorithm especially with the case of overlapping clusters.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 29, Issue 4, 1 March 2008, Pages 515–524
نویسندگان
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