دانلود مقاله ISI انگلیسی شماره 79052
ترجمه فارسی عنوان مقاله

انتخاب الگوریتم خوشه بندی سیستم های متا یادگیری: خصوصیات مشکل مبتنی بر فاصله جدید و روش های ترکیبی رتبه بندی

عنوان انگلیسی
Clustering algorithm selection by meta-learning systems: A new distance-based problem characterization and ranking combination methods
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
79052 2015 14 صفحه PDF
منبع

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

Journal : Information Sciences, Volume 301, 20 April 2015, Pages 181–194

ترجمه کلمات کلیدی
خوشه بندی؛ خصوصیات مشکل؛ الگوریتم رتبه بندی؛ انتخاب الگوریتم؛ فرا دانش؛ سیستم های متا یادگیری
کلمات کلیدی انگلیسی
Clustering; Problem characterization; Algorithm ranking; Algorithm selection; Meta-knowledge; Meta-learning systems
پیش نمایش مقاله
پیش نمایش مقاله  انتخاب الگوریتم خوشه بندی سیستم های متا یادگیری: خصوصیات مشکل مبتنی بر فاصله جدید و روش های ترکیبی رتبه بندی

چکیده انگلیسی

Data clustering aims to segment a database into groups of objects based on the similarity among these objects. Due to its unsupervised nature, the search for a good-quality solution can become a complex process. There is currently a wide range of clustering algorithms, and selecting the best one for a given problem can be a slow and costly process. In 1976, Rice formulated the Algorithm Selection Problem (ASP), which postulates that the algorithm performance can be predicted based on the structural characteristics of the problem. Meta-learning brings the concept of learning about learning; that is, the meta-knowledge obtained from the algorithm learning process allows the improvement of the algorithm performance. Meta-learning has a major intersection with data mining in classification problems, in which it is normally used to recommend algorithms. The present paper proposes new ways to obtain meta-knowledge for clustering tasks. Specifically, two contributions are explored here: (1) a new approach to characterize clustering problems based on the similarity among objects; and (2) new methods to combine internal indices for ranking algorithms based on their performance on the problems. Experiments were conducted to evaluate the recommendation quality. The results show that the new meta-knowledge provides high-quality algorithm selection for clustering tasks.