کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10361341 | 870186 | 2005 | 14 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Enhanced neural gas network for prototype-based clustering
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
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چکیده انگلیسی
In practical cluster analysis tasks, an efficient clustering algorithm should be less sensitive to parameter configurations and tolerate the existence of outliers. Based on the neural gas (NG) network framework, we propose an efficient prototype-based clustering (PBC) algorithm called enhanced neural gas (ENG) network. Several problems associated with the traditional PBC algorithms and original NG algorithm such as sensitivity to initialization, sensitivity to input sequence ordering and the adverse influence from outliers can be effectively tackled in our new scheme. In addition, our new algorithm can establish the topology relationships among the prototypes and all topology-wise badly located prototypes can be relocated to represent more meaningful regions. Experimental results1on synthetic and UCI datasets show that our algorithm possesses superior performance in comparison to several PBC algorithms and their improved variants, such as hard c-means, fuzzy c-means, NG, fuzzy possibilistic c-means, credibilistic fuzzy c-means, hard/fuzzy robust clustering and alternative hard/fuzzy c-means, in static data clustering tasks with a fixed number of prototypes.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 38, Issue 8, August 2005, Pages 1275-1288
Journal: Pattern Recognition - Volume 38, Issue 8, August 2005, Pages 1275-1288
نویسندگان
A.K. Qin, P.N. Suganthan,