Analysis on KPI factors to choose lands with fuzzy ISODATA clustering

Chengjie Li, Zhen Liu
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Abstract

Clustering is an example of a class of optimization problems. In the classical clustering, an item must belong to any one cluster. But fuzzy clustering describes more accurately the ambiguous type of structure in data. The fuzzy ISODATA clustering exhibits the rapid convergence in finding the best classification program when the classification number is given. In this paper, we propose the algorithm to solve the choosing lands problem and show the result of the experiment. The result is satisfied to realtors in choosing lands.
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基于模糊ISODATA聚类的土地选择KPI因素分析
聚类是一类优化问题的一个例子。在经典聚类中,一个项必须属于任意一个聚类。而模糊聚类更准确地描述了数据中的模糊结构类型。在给定分类数的情况下,模糊ISODATA聚类在寻找最佳分类方案方面具有较快的收敛性。本文提出了一种求解土地选择问题的算法,并给出了实验结果。研究结果使房地产经纪人在选择土地时感到满意。
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