کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10429148 | 909697 | 2005 | 5 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Improvement and Parallelism of k-Means Clustering Algorithm
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی (عمومی)
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
The k-means clustering algorithm is one of the most commonly used algorithms for clustering analysis. The traditional k-means algorithm is, however, inefficient while working on large numbers of data sets and improving the algorithm efficiency remains a problem. This paper focuses on the efficiency issues of cluster algorithms. A refined initial cluster centers method is designed to reduce the number of iterative procedures in the algorithm. A parallel k-means algorithm is also studied for the problem of the operation limitation of a single processor machine when given huge data sets. The analytical results demonstrate that these improvements can greatly enhance the efficiency of the k-means algorithm, i.e., allow the grouping of a large number of data sets more accurately and more quickly. The analysis has theoretical and practical importance for work on the improvement and parallelism of cluster algorithms.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Tsinghua Science & Technology - Volume 10, Issue 3, June 2005, Pages 277-281
Journal: Tsinghua Science & Technology - Volume 10, Issue 3, June 2005, Pages 277-281
نویسندگان
Tian (ç°éå
°), Zhu (æ± æ), Zhang (å¼ ç´ ç´), Liu (å ç),