دانلود مقاله ISI انگلیسی شماره 79194
عنوان فارسی مقاله

یک الگوریتم خوشه بندی جدید بر اساس بهینه سازی جهانی ترکیبی بر اساس یک الگوریتم رویکرد سیستم های دینامیکی

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
79194 2010 8 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
A new clustering algorithm based on hybrid global optimizationbased on a dynamical systems approach algorithm
منبع

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

Journal : Expert Systems with Applications, Volume 37, Issue 8, August 2010, Pages 5645–5652

کلمات کلیدی
خوشه بندی؛ K-means؛ سیستم های دینامیکی؛ جستجوی ممنوع
پیش نمایش مقاله
پیش نمایش مقاله یک الگوریتم خوشه بندی جدید بر اساس بهینه سازی جهانی ترکیبی بر اساس یک الگوریتم رویکرد سیستم های دینامیکی

چکیده انگلیسی

Many methods for local optimization are based on the notion of a direction of a local descent at a given point. A local improvement of a point in hand can be made using this direction. As a rule, modern methods for global optimization do not use directions of global descent for global improvement of the point in hand. From this point of view, global optimization algorithm based on a dynamical systems approach (GOP) is an unusual method. Its structure is similar to that used in local optimization: a new iteration can be obtained as an improvement of the previous one along a certain direction. In contrast with local methods, is a direction of a global descent and for more diversification combined with Tabu search. This algorithm is called hybrid GOP (HGOP). Cluster analysis is one of the attractive data mining techniques that are used in many fields. One popular class of data clustering algorithms is the center based clustering algorithm. K-means is used as a popular clustering method due to its simplicity and high speed in clustering large datasets. However, K-means has two shortcomings: dependency on the initial state and convergence to local optima and global solutions of large problems cannot found with reasonable amount of computation effort. In order to overcome local optima problem lots of studies have been done in clustering. In this paper, we proposed application of hybrid global optimization algorithm based on a dynamical systems approach. We compared HGOP with other algorithms in clustering, such as GAK, SA, TS, and ACO, by implementing them on several simulation and real datasets. Our finding shows that the proposed algorithm works better than others.

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