A Spatial Entropy reflecting Distribution of Spatial Objects
Decision trees are mainly used to classify data and predict data classes. A spatial decision tree has been designed using Euclidean distance between objects for reflecting spatial data characteristic. Even though this method explains the distance of objects in spatial dimension, it fails to represent distributions of spatial data and their relationships. But distributions of spatial data and relationships with their neighborhoods are very important in real world. This paper proposes decision tree based on spatial entropy that represents distributions of spatial data with dispersion and dissimilarity. The rate of dispersion by dissimilarity presents how related distribution of spatial data and non-spatial attributes. Our experiment evaluates accuracy and building time of decision tree as compared to previous methods and it shows that proposed method makes efficient and scalable classification for spatial decision support.
Youn-Kyung Jang Byeong-Seob You Ho-Seok Kim Kyoung-Bae Kim Hae-Young Bae
Dept. of Computer Science and Information Engineering, lnha University 253 Younghyun-dong, Nam-ku, I Department of Computer Education, Seowon University 231 Mochung-dong Heungduk-gu Cheongju-si Chungbu
国际会议
重庆
英文
206-212
2007-04-24（万方平台首次上网日期，不代表论文的发表时间）