TY - JOUR
T1 - An adaptive selection strategy and separation measure for improving the wu-huberman clustering
AU - Sun, Yan
AU - Tang, Yiyuan
AU - Yang, Lizhu
PY - 2012
Y1 - 2012
N2 - Clustering is an old research topic in data mining and machine learning. There are so many clustering methods, and among them Wu-Huberman is few linear algorithm. In this paper, we propose the CBWH algorithm that does not only preserve the merit of Wu-Huberman, but also increase the robustness and extend the application of the Wu-Huberman. At first, we extend the Wu-Huberman to the general clustering problem by constructing graph. Then, we present a new idea to determine the threshold of the clustering. Additionally, we provide experiments for analyzing the effectiveness of the algorithm, comparing with other related algorithms and discussing the sensitivity, from which we find that CBWH is more robust.
AB - Clustering is an old research topic in data mining and machine learning. There are so many clustering methods, and among them Wu-Huberman is few linear algorithm. In this paper, we propose the CBWH algorithm that does not only preserve the merit of Wu-Huberman, but also increase the robustness and extend the application of the Wu-Huberman. At first, we extend the Wu-Huberman to the general clustering problem by constructing graph. Then, we present a new idea to determine the threshold of the clustering. Additionally, we provide experiments for analyzing the effectiveness of the algorithm, comparing with other related algorithms and discussing the sensitivity, from which we find that CBWH is more robust.
KW - Clustering
KW - Data mining
KW - KNN (k nearest neighborhood)
KW - Wu-Huberman
UR - http://www.scopus.com/inward/record.url?scp=84870767446&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:84870767446
SN - 2185-2766
VL - 3
SP - 1531
EP - 1536
JO - ICIC Express Letters, Part B: Applications
JF - ICIC Express Letters, Part B: Applications
IS - 6
ER -