کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4948447 | 1439613 | 2016 | 55 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
An integrated K-means - Laplacian cluster ensemble approach for document datasets
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Cluster ensemble has become an important extension to traditional clustering algorithms, yet the cluster ensemble problem is very challenging due to the inherent difficulty in resolving the label correspondence problem. We adapted the integrated K-means - Laplacian clustering approach to solve the cluster ensemble problem by exploiting both the attribute information embedded in the cluster labels and the pairwise relations among the objects. The optimal solution of the proposed approach requires computing the pseudo inverse of the normalized Laplacian matrix and the eigenvalue decomposition of a large matrix, which can be computationally burdensome for large scale document datasets. We devised an effective algebraic transformation method for efficiently carrying out the aforementioned computations and proposed an integrated K-means - Laplacian cluster ensemble approach (IKLCEA). Experimental results with benchmark document datasets demonstrate that IKLCEA outperforms other cluster ensemble techniques on most cases. In addition, IKLCEA is computationally efficient and can be readily employed in large scale document applications.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 214, 19 November 2016, Pages 495-507
Journal: Neurocomputing - Volume 214, 19 November 2016, Pages 495-507
نویسندگان
Sen Xu, Kung-Sik Chan, Jun Gao, Xiufang Xu, Xianfeng Li, Xiaopeng Hua, Jing An,