دانلود مقاله ISI انگلیسی شماره 46215
عنوان فارسی مقاله

روش شناسی دسته بندی بر پایه الگوریتم مورچگان

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
46215 2015 11 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
Ant Colony Optimization based clustering methodology
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Applied Soft Computing, Volume 28, March 2015, Pages 301–311

کلمات کلیدی
خوشه بندی - بهینه سازی کلونی مورچه - اهداف چندگانه - کاهش مجموعه داده
پیش نمایش مقاله
پیش نمایش مقاله روش شناسی دسته بندی بر پایه الگوریتم مورچگان

چکیده انگلیسی

In this work we consider spatial clustering problem with no a priori information. The number of clusters is unknown, and clusters may have arbitrary shapes and density differences. The proposed clustering methodology addresses several challenges of the clustering problem including solution evaluation, neighborhood construction, and data set reduction. In this context, we first introduce two objective functions, namely adjusted compactness and relative separation. Each objective function evaluates the clustering solution with respect to the local characteristics of the neighborhoods. This allows us to measure the quality of a wide range of clustering solutions without a priori information. Next, using the two objective functions we present a novel clustering methodology based on Ant Colony Optimization (ACO-C). ACO-C works in a multi-objective setting and yields a set of non-dominated solutions. ACO-C has two pre-processing steps: neighborhood construction and data set reduction. The former extracts the local characteristics of data points, whereas the latter is used for scalability. We compare the proposed methodology with other clustering approaches. The experimental results indicate that ACO-C outperforms the competing approaches. The multi-objective evaluation mechanism relative to the neighborhoods enhances the extraction of the arbitrary-shaped clusters having density variations.

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