An Improved Rough Clustering Using Discernibility Based Initial Seed Computation

摘要：

In this paper, we present the discernibility approach for an initial seed computation of Rough K-Means (RKM). We propose the use of the discernibility initial seed computation (ISC) for RKM. Our proposed algorithm aims to improve the performance and to avoid the problem of an empty cluster which affects the numerical stability since there are data constellations where |Ck| = 0 in RKM algorithm. For verification, our proposed algorithm was tested using 8 UCI datasets and validated using the David Bouldin Index. The experimental results showed that the proposed algorithm of the discernibility initial seed computation of RKM was appropriate to avoid the empty cluster and capable of improving the performance of RKM.

关键词： Discernibility Initial Seed Computation Rough K-Means

作者: Djoko Budiyanto Setyohadi Azuraliza Abu Bakar Zulaiha Ali Othman

作者单位: Center for Artificial Intelligence Technology University Kebangsaan Malaysia Bangi,Selangor Darul Ehsan 43000 Malaysia

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 161-168

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）