期刊名称：Journal of International Technology and Information Management
印刷版ISSN：1941-6679
出版年度：2015
卷号：24
期号：1
页码：2
出版社：California State University, San Bernardino
摘要：A main obstacle to accurate prediction is often the heterogeneous nature of data. Existing studies have pointed to data clustering as a potential solution to reduce heterogeneity, and therefore increase prediction accuracy. This paper describes an innovative clustering approach based on a novel adaptation of the Fuzzy C-Means algorithm and its application to market segmentation in real estate. Over 15,000 actual home sales transactions were used to evaluate our approach. The test results demonstrate that the accuracy in price prediction shows notable improvement for some clustered market segments. In comparison with existing methods our approach is simple to implement. It does not require additional collection of data or costly development of models to incorporate social-economic factors on segmentation. Finally our approach is not market specific and can be easily applied across different housing markets.
关键词：Fuzzy c-means clustering; mass assessment; k-means clustering; real estate submarkets; cluster homogeneity; ANFIS
