TY - GEN
T1 - BrainQuest
T2 - 15th IEEE International Conference on Data Mining, ICDM 2015
AU - Shi, Lei
AU - Tong, Hanghang
AU - Mu, Xinzhu
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/1/5
Y1 - 2016/1/5
N2 - Why are some people more creative than others? How do human brain networks evolve over time? A key stepping stone to both mysteries and many more is to compare weighted brain networks. In contrast to networks arising from other application domains, the brain network exhibits its own characteristics (e.g., high density, indistinguishability), which makes any off-the-shelf data mining algorithm as well as visualization tool sub-optimal or even mis-leading. In this paper, we propose a shift from the current mining-then-visualization paradigm, to jointly model these two core building blocks (i.e., mining and visualization) for brain network comparisons. The key idea is to integrate the human perception constraint into the mining block earlier so as to guide the analysis process. We formulate this as a multi-objective feature selection problem, and propose an integrated framework, BrainQuest, to solve it. We perform extensive empirical evaluations, both quantitatively and qualitatively, to demonstrate the effectiveness and efficiency of our approach.
AB - Why are some people more creative than others? How do human brain networks evolve over time? A key stepping stone to both mysteries and many more is to compare weighted brain networks. In contrast to networks arising from other application domains, the brain network exhibits its own characteristics (e.g., high density, indistinguishability), which makes any off-the-shelf data mining algorithm as well as visualization tool sub-optimal or even mis-leading. In this paper, we propose a shift from the current mining-then-visualization paradigm, to jointly model these two core building blocks (i.e., mining and visualization) for brain network comparisons. The key idea is to integrate the human perception constraint into the mining block earlier so as to guide the analysis process. We formulate this as a multi-objective feature selection problem, and propose an integrated framework, BrainQuest, to solve it. We perform extensive empirical evaluations, both quantitatively and qualitatively, to demonstrate the effectiveness and efficiency of our approach.
UR - https://www.scopus.com/pages/publications/84963571401
U2 - 10.1109/ICDM.2015.135
DO - 10.1109/ICDM.2015.135
M3 - 会议稿件
AN - SCOPUS:84963571401
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 379
EP - 388
BT - Proceedings - 15th IEEE International Conference on Data Mining, ICDM 2015
A2 - Aggarwal, Charu
A2 - Zhou, Zhi-Hua
A2 - Tuzhilin, Alexander
A2 - Xiong, Hui
A2 - Wu, Xindong
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 14 November 2015 through 17 November 2015
ER -