دانلود مقاله ISI انگلیسی شماره 79056
عنوان فارسی مقاله

یک الگوریتم خوشه بندی اتوماتیک تعمیم در چارچوب چند هدفه

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
79056 2013 20 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
A generalized automatic clustering algorithm in a multiobjective framework
منبع

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

Journal : Applied Soft Computing, Volume 13, Issue 1, January 2013, Pages 89–108

کلمات کلیدی
خوشه بندی؛ بهینه سازی چند هدفه (MOO)؛ تقارن؛ نمودار محله نسبی؛ چند مرکزی؛ تعیین خودکار از تعداد خوشه
پیش نمایش مقاله
پیش نمایش مقاله یک الگوریتم خوشه بندی اتوماتیک تعمیم در چارچوب چند هدفه

چکیده انگلیسی

In this paper a new multiobjective (MO) clustering technique (GenClustMOO) is proposed which can automatically partition the data into an appropriate number of clusters. Each cluster is divided into several small hyperspherical subclusters and the centers of all these small sub-clusters are encoded in a string to represent the whole clustering. For assigning points to different clusters, these local sub-clusters are considered individually. For the purpose of objective function evaluation, these sub-clusters are merged appropriately to form a variable number of global clusters. Three objective functions, one reflecting the total compactness of the partitioning based on the Euclidean distance, the other reflecting the total symmetry of the clusters, and the last reflecting the cluster connectedness, are considered here. These are optimized simultaneously using AMOSA, a newly developed simulated annealing based multiobjective optimization method, in order to detect the appropriate number of clusters as well as the appropriate partitioning. The symmetry present in a partitioning is measured using a newly developed point symmetry based distance. Connectedness present in a partitioning is measured using the relative neighborhood graph concept. Since AMOSA, as well as any other MO optimization technique, provides a set of Pareto-optimal solutions, a new method is also developed to determine a single solution from this set. Thus the proposed GenClustMOO is able to detect the appropriate number of clusters and the appropriate partitioning from data sets having either well-separated clusters of any shape or symmetrical clusters with or without overlaps. The effectiveness of the proposed GenClustMOO in comparison with another recent multiobjective clustering technique (MOCK), a single objective genetic algorithm based automatic clustering technique (VGAPS-clustering), K-means and single linkage clustering techniques is comprehensively demonstrated for nineteen artificial and seven real-life data sets of varying complexities. In a part of the experiment the effectiveness of AMOSA as the underlying optimization technique in GenClustMOO is also demonstrated in comparison to another evolutionary MO algorithm, PESA2.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.