MaJChengJCP.Data-driven study on the achievement of LEED credits using percentage of average score and association rule analysis. Build Environ2016;
98: 121–132.
2.
JunMAChengJCP.Selection of target LEED credits based on project information and climatic factors using data mining techniques. Adv Eng Inf2017;
32: 224–236.
3.
ChengJCPMaLJ.A data-driven study of important climate factors on the achievement of LEED-EB credits. Build Environ2015;
90: 232–244.
4.
ChengJCPMaLJ.A non-linear case-based reasoning approach for retrieval of similar cases and selection of target credits in LEED projects. Build Environ2015;
93: 349–361.
5.
LiY.Remotely sense data segmentation under a spatial statistics framework.
Waterloo:
University of Waterloo, 2010.
6.
MaJChengJCP.Estimation of the building energy use intensity in the urban scale by integrating GIS and big data technology. Appl Energy2016;
183: 182–192.
7.
MaJChengJCP.Identifying the influential features on the regional energy use intensity of residential buildings based on random forests. Appl Energy2016;
183: 193–201.
8.
MaJChengJCP.Identification of the numerical patterns behind the leading counties in the U.S. local green building markets using data mining. J Clean Prod2017;
151: 406–418.
9.
AskariGAigongXYuL, et al.
Automatic determination of number of homogenous regions in SAR images utilizing splitting and merging based on a reversible jump MCMC algorithm. J Ind Soc Remote Sens2013;
41: 509–521.
10.
QuanhuaZXiaoLXuemeiZ, et al.
Fuzzy ISODATA image segmentation integrating Voronoi tessellation HMRF model. J Signal Process2016;
32: 1233–1243.
11.
BoPLeiZZhangD.A survey of graph theoretical approaches to image segmentation. Pattern Recogn2013;
46: 1020–1038.
12.
Zhaocong W, Zhongwen H, Qian Z, et al. On combining spectral, textural and shape features for remote sensing image segmentation. Acta Geod Cartogr Sin 2013;
42: 44–50.
13.
HuiLYunweiTQingjieL, et al.
An improved algorithm based on minimum spanning tree for multi-scale segmentation of remote sensing imagery. Acta Geod Cartogr Sin2015;
44: 791–796.
14.
PingWZhengWWeihongC, et al.
An image segmentation method based on statistics learning theory and minimum spanning tree. Geomat Inf Sci Wuhan Univ2017;
42: 877–882.
15.
YingLFangDYongH, et al.
Image segmentation based on minimum spanning tree. Comput Eng Appl2013;
49: 149–151.
16.
QuanhuaZXiaoLXuemeiZ, et al.
Remote sensing image segmentation algorithm with regional fuzzy cluster and Mahalanobis distance. J China Univ Mining Technol2017;
46: 222–228.
17.
Lucarini V.
Symmetry-break in Voronoi tessellations.Symmetry2009;
1: 21–54.
18.
AchantaRShajiASmithK, et al.
SLIC superpixels compared to state-of-the-art superpixel methods.IEEE Trans Pattern Anal Mach Intell2012;
34: 2274–2282.
19.
BanerjeeBVarmaSBuddhirajuKM, et al.
Unsupervised multi-spectral satellite image segmentation Combining modified mean-shift and a new minimum spanning tree based clustering technique. IEEE J Select Top Appl Earth Observ Remote Sens2014;
7: 888–894.
20.
KangRLinYWangY, et al.
A pedagogical case on active learning regarding to Kirchhoff’s circuit laws. Int J Electr Eng Educ2019;
56: 179–190.
21.
PerotoniMBVieiraMS.Increasing the interest in antennas and microwave courses.Int J Electr Eng Educ2019;
56: 193–207.
