کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
471561 698643 2006 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Rough Set-Based Clustering with Refinement Using Shannon's Entropy Theory
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر علوم کامپیوتر (عمومی)
پیش نمایش صفحه اول مقاله
Rough Set-Based Clustering with Refinement Using Shannon's Entropy Theory
چکیده انگلیسی

Lots of clustering algorithms have been developed, while most of them cannot process objects in hybrid numerical/nominal attribute space or with missing values. In most of them, the number of clusters should be manually determined and the clustering results are sensitive to the input order of the objects to be clustered. These limit applicability of the clustering and reduce the quality of clustering. To solve this problem, an improved clustering algorithm based on rough set (RS) and entropy theory was presented. It aims at avoiding the need to prespecify the number of clusters, and clustering in both numerical and nominal attribute space with the similarity introduced to replace the distance index. At the same time, the RS theory endows the algorithm with the function to deal with vagueness and uncertainty in data analysis. Shannon's entropy was used to refine the clustering results by assigning relative weights to the set of attributes according to the mutual entropy values. A novel measure of clustering quality was also presented to evaluate the clusters. This algorithm was analyzed and applied later to cluster the data set of one industrial product. The experimental results confirm that performances of efficiency and clustering quality of this algorithm are improved.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Mathematics with Applications - Volume 52, Issues 10–11, November–December 2006, Pages 1563-1576