A Study on the Fuzzy Market Segment of Purchaser of Real Estate

Clustering is suitable for mining unknown data because of its exploratory ability and firm theory basis. The theory is based on crisp partition form. However, in practice, data are usually not well distributed, and may be included with many outliers. Due to the fact that the requirements on the consumers and the market are very high and many real-market problems are fuzzy by nature and not random, the probability applications have not been very satisfactory in a lot of cases. In this study, we adopt the fuzzy cluster method and attempt to combine it with new compactness and separation validity function in order to build a market segmentation, which can address the fuzziness among the group boundaries. Then, we can use membership grade to describe each group. Consequently, the real market situation can be clearly presented. Through membership grade we depict the reality of the market, which lies between integers and real numbers. Buyers' mindset are both rational and complicate, so they're purchasing decisions cannot be predictable and will be affected by many factors. The structural stability of the market can be tested by the loyalty of buyers who pertain to different clusters. Marketing strategies will also have effects on the movement of group for housing buyer.