CAJ | 학술논문

数据挖掘技术中的聚类算法是解决客户细分问题的重要算法之一。为解决传统聚类算法在客户细分问题中分类精度较低、收敛速度较慢的问题，着重对比分析传统聚类算法中K-means、自组织映射网络和粒子群3种算法的不足，提出融合3种算法优点的混合型聚类算法，该算法利用K—means和自组织映射网络对初始聚类中心进行优化，结合粒子群优化和K-means优化聚类迭代过程，并在迭代优化过程中设计避免算法因早熟而停滞的机制。针对移动电子商务环境下的餐饮业客户细分问题，建立移动餐饮业客户细分模型，并利用混合型聚类算法、K-means、层级自组织映射网络和基于粒子群的K-means等4种算法对实际案例进行对比分析。研究结果表明，混合型聚类算法的聚类精度分别比其他3种算法高，同时还具有最快的收敛性能，更适用于客户细分问题。
수거알굴기술중적취류산법시해결객호세분문제적중요산법지일。위해결전통취류산법재객호세분문제중분류정도교저、수렴속도교만적문제，착중대비분석전통취류산법중K-means、자조직영사망락화입자군3충산법적불족，제출융합3충산법우점적혼합형취류산법，해산법이용K—means화자조직영사망락대초시취류중심진행우화，결합입자군우화화K-means우화취류질대과정，병재질대우화과정중설계피면산법인조숙이정체적궤제。침대이동전자상무배경하적찬음업객호세분문제，건립이동찬음업객호세분모형，병이용혼합형취류산법、K-means、층급자조직영사망락화기우입자군적K-means등4충산법대실제안례진행대비분석。연구결과표명，혼합형취류산법적취류정도분별비기타3충산법고，동시환구유최쾌적수렴성능，경괄용우객호세분문제。
Clustering algorithms in data mining technology is an important kind of algorithms of soloving customer segmentation problems. To overcome the low accuracy and slow convergence of traditional clustering algorithms in customer segmentation, this paper analyzes deficiencies of traditional cluster algorithms, K-means, SOM and PSO. After that, an improved hybrid clustering algorithm named KSP is proposed, which integrates advantages of K-means, SOM and PSO. The initialization of KSP is optimized by K-means and SOM ; the solving process is carried out by the combination of PSO and K-means with a mechanism of restraining premature stagnancy. Then, a customer segmentation model was established to analyze types of customers in catering industry under mobile electronic commerce environment. Also, an actual case was illustrated to verify the efficiency of the KSP algorithm. The results show that the KSP has the highest accuracy and convergence rate. Thus, it is more suitable for customer segmentation.