- 期刊
- OpenAccess
History and Potential of Binary Segmentation for Exploratory Data Analysis
並列摘要
Exploratory data analysis has become more important as large rich data sets become available, with many explanatory variables representing competing theoretical constructs. The restrictive assumptions of linearity and additivity of effects as in regression are no longer necessary to save degrees of freedom. Where there is a clear criterion (dependent) variable or classification, sequential binary segmentation (tree) programs are being used. We explain why, using the current enhanced version (SEARCH) of the original Automatic Interaction Detector program as an illustration. Even the simple example uncovers an interaction that might well have been missed with the usual multivariate regression. We then suggest some promising uses and provide one simple example.
被引用紀錄
延伸閱讀
- Nguyen, D. V., Wang, N. S., & Carroll, R. J. (2004). Evaluation of Missing Value Estimation for Microarray Data. Journal of Data Science, 2(4), 347-370. https://doi.org/10.6339/JDS.2004.02(4).170
- 傅國瑋、許玉雪（2020）。An Application of Machine Learning Classification Algorithms in Prediction of Binary Imbalanced Data。統計與資訊評論，20()，69-92。https://www.airitilibrary.com/Article/Detail?DocID=10269614-202012-202105130011-202105130011-69-92
- 曾崇儀（2006）。A Study on Binary Search Algorithm for VQ〔碩士論文，國立臺北科技大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0006-1207200610465400
- 柳琬萱（2022）。Exploratory data analysis system for time series geographical data〔碩士論文，淡江大學〕。華藝線上圖書館。https://www.airitilibrary.com/Article/Detail?DocID=U0002-2507202216160500
- Peng, Y., Xu, W., & Xiong, Y. (2021). Critical analysis of Big Data challenges on similar sequential Algorithm based data Search. 淡江理工學刊, 24(4), 653-659. https://doi.org/10.6180/jase.202108_24(4).0021