Fuzzy self-organizing maps for data mining with incomplete data sets
Self-organizing maps (SOM) have become a commonly-used cluster analysis technique in data mining. However, SOM are not able to process incomplete data. To build more capability of data mining for SOM, this study proposes an SOM-based fuzzy map model for data mining with incomplete data sets. Using this model, incomplete data are translated into fuzzy data, and are used to generate fuzzy observations. These fuzzy observations, along with observations without missing values, are then used to train the SOM to generate fuzzy maps. Compared with the standard SOM approach, fuzzy maps generated by the proposed method can provide more information for knowledge discovery.
fuzzy clustering incomplete data self-organizing maps
Shidong YU Hang LI Qi XU Xianfeng WU
College of Software Shenyang Normal University Shenyang 110034, China College of Physics Shenyang Normal University Shenyang 110034, China Shenyang CBMP XINDA Banking Equipment CO.,Ltd.Shenyang 110010, China
国际会议
太原
英文
336-340
2010-10-22（万方平台首次上网日期，不代表论文的发表时间）