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    • 1. 发明申请
    • CLUSTERING CUSTOMERS
    • 聚集客户
    • US20120290580A1
    • 2012-11-15
    • US13561468
    • 2012-07-30
    • Heng CaoJin DongJacqueline Giang Huong MorrisMing XieWen Jun YinBin Zhang
    • Heng CaoJin DongJacqueline Giang Huong MorrisMing XieWen Jun YinBin Zhang
    • G06F17/30
    • G06Q30/02
    • A computer implemented method for clustering customers includes receiving a source set of customer records, wherein each customer record represents one customer, and each customer record includes at least one data attribute, and each data attribute has an attribute value; pre-processing the source set of customer records to generate a pre-processed set of customer records; executing a clustering algorithm on the pre-processed set of customer records to group the pre-processed set of customer records into clusters of a pre-defined number. The pre-processing comprises: determining the type of a customer in the source set of customer records; using a type attribute value to indicate the type of the customer in its customer record; normalizing data attribute values and type attribute values; weighting to the data attribute values and the type attribute values respectively to obtain weighted attribute values of the data attribute and weighted attribute values of the type attribute.
    • 用于聚类客户的计算机实现方法包括接收客户记录的源集合,其中每个客户记录表示一个客户,并且每个客户记录包括至少一个数据属性,并且每个数据属性具有属性值; 预处理客户记录的源集合以生成预处理的一组客户记录; 在预处理的客户记录集上执行聚类算法,以将预处理的客户记录集合分组成预定义数量的集群。 预处理包括:确定客户记录源组中客户的类型; 使用类型属性值来指示客户记录中客户的类型; 归一化数据属性值和类型属性值; 分别对数据属性值和类型属性值进行加权,以获得类型属性的数据属性和加权属性值的加权属性值。
    • 2. 发明授权
    • Clustering customers
    • 聚集客户
    • US08918397B2
    • 2014-12-23
    • US13561468
    • 2012-07-30
    • Heng CaoJin DongJacqueline Giang Huong MorrisMing XieWen Jun YinBin Zhang
    • Heng CaoJin DongJacqueline Giang Huong MorrisMing XieWen Jun YinBin Zhang
    • G06F17/30G06Q30/02
    • G06Q30/02
    • A computer implemented method for clustering customers includes receiving a source set of customer records, wherein each customer record represents one customer, and each customer record includes at least one data attribute, and each data attribute has an attribute value; pre-processing the source set of customer records to generate a pre-processed set of customer records; executing a clustering algorithm on the pre-processed set of customer records to group the pre-processed set of customer records into clusters of a pre-defined number. The pre-processing comprises: determining the type of a customer in the source set of customer records; using a type attribute value to indicate the type of the customer in its customer record; normalizing data attribute values and type attribute values; weighting to the data attribute values and the type attribute values respectively to obtain weighted attribute values of the data attribute and weighted attribute values of the type attribute.
    • 用于聚类客户的计算机实现方法包括接收客户记录的源集合,其中每个客户记录表示一个客户,并且每个客户记录包括至少一个数据属性,并且每个数据属性具有属性值; 预处理客户记录的源集合以生成预处理的一组客户记录; 在预处理的客户记录集上执行聚类算法,以将预处理的客户记录集合分组成预定义数量的集群。 预处理包括:确定客户记录源组中客户的类型; 使用类型属性值来指示客户记录中客户的类型; 归一化数据属性值和类型属性值; 分别对数据属性值和类型属性值进行加权,以获得类型属性的数据属性和加权属性值的加权属性值。
    • 3. 发明授权
    • Clustering customers
    • 聚集客户
    • US08914372B2
    • 2014-12-16
    • US13432361
    • 2012-03-28
    • Heng CaoJin DongJacqueline Giang Huong MorrisMing XieWen Jun YinBin Zhang
    • Heng CaoJin DongJacqueline Giang Huong MorrisMing XieWen Jun YinBin Zhang
    • G06F17/30G06Q30/02
    • G06Q30/02
    • A computer implemented method for clustering customers includes receiving a source set of customer records, wherein each customer record represents one customer, and each customer record includes at least one data attribute, and each data attribute has an attribute value; pre-processing the source set of customer records to generate a pre-processed set of customer records; executing a clustering algorithm on the pre-processed set of customer records to group the pre-processed set of customer records into clusters of a pre-defined number. The pre-processing comprises: determining the type of a customer in the source set of customer records; using a type attribute value to indicate the type of the customer in its customer record; normalizing data attribute values and type attribute values; weighting to the data attribute values and the type attribute values respectively to obtain weighted attribute values of the data attribute and weighted attribute values of the tune attribute.
    • 用于聚类客户的计算机实现方法包括接收客户记录的源集合,其中每个客户记录表示一个客户,并且每个客户记录包括至少一个数据属性,并且每个数据属性具有属性值; 预处理客户记录的源集合以生成预处理的一组客户记录; 在预处理的客户记录集上执行聚类算法,以将预处理的客户记录集合分组成预定义数量的集群。 预处理包括:确定客户记录源组中客户的类型; 使用类型属性值来指示客户记录中客户的类型; 归一化数据属性值和类型属性值; 分别对数据属性值和类型属性值进行加权,以获得数据属性的加权属性值和调整属性的加权属性值。
    • 4. 发明申请
    • CLUSTERING CUSTOMERS
    • 聚集客户
    • US20120254179A1
    • 2012-10-04
    • US13432361
    • 2012-03-28
    • Heng CaoJin DongJacqueline Giang Huong MorrisMing XieWen Jun YinBin Zhang
    • Heng CaoJin DongJacqueline Giang Huong MorrisMing XieWen Jun YinBin Zhang
    • G06F17/30
    • G06Q30/02
    • A computer implemented method for clustering customers includes receiving a source set of customer records, wherein each customer record represents one customer, and each customer record includes at least one data attribute, and each data attribute has an attribute value; pre-processing the source set of customer records to generate a pre-processed set of customer records; executing a clustering algorithm on the pre-processed set of customer records to group the pre-processed set of customer records into clusters of a pre-defined number. The pre-processing comprises: determining the type of a customer in the source set of customer records; using a type attribute value to indicate the type of the customer in its customer record; normalizing data attribute values and type attribute values; weighting to the data attribute values and the type attribute values respectively to obtain weighted attribute values of the data attribute and weighted attribute values of the tune attribute.
    • 用于聚类客户的计算机实现方法包括接收客户记录的源集合,其中每个客户记录表示一个客户,并且每个客户记录包括至少一个数据属性,并且每个数据属性具有属性值; 预处理客户记录的源集合以生成预处理的一组客户记录; 在预处理的客户记录集上执行聚类算法,以将预处理的客户记录集合分组成预定义数量的集群。 预处理包括:确定客户记录源组中客户的类型; 使用类型属性值来指示客户记录中客户的类型; 归一化数据属性值和类型属性值; 分别对数据属性值和类型属性值进行加权,以获得数据属性的加权属性值和调整属性的加权属性值。
    • 7. 发明授权
    • Method and apparatus for unified optimization model for retail network configuration
    • 零售网络配置统一优化模型的方法和装置
    • US08265984B2
    • 2012-09-11
    • US12117540
    • 2008-05-08
    • Xin Xin BaiJin DongTa-Hsin LiMing XieWen Jun YinBin ZhangCindy Q. Zhang
    • Xin Xin BaiJin DongTa-Hsin LiMing XieWen Jun YinBin ZhangCindy Q. Zhang
    • G06Q30/00
    • G06Q10/04G06Q10/0637
    • A method and system for integrating multiple factors into a unified optimization model for retail network configuration, in one aspect, obtains input data for modeling store configuration. The input data may include demand of each merchandise category from each customer segment in each facility, geographic distribution of stores in an area, current revenue of stores, and physical cost of reconfiguring stores. A trade area is generated as a function of store location, store format, and store capacity. The method and system also generates trade area demand summation representing predicted total demand of all stores for all merchandise categories for all customer segments in the trade area, as a function of store location, store format, store capacity, merchandise category, and customer segment associated with the trade area. An objective function is constructed as a function of said trade area demand summation, current revenue of stores, and physical cost of reconfiguring stores.
    • 一种将多个因素整合到零售网络配置的统一优化模型中的方法和系统,一方面获取用于建模商店配置的输入数据。 输入数据可以包括每个设施中的每个客户段的每个商品类别的需求,区域中的商店的地理分布,商店的当前收入以及重新配置商店的物理成本。 作为商店位置,商店格式和商店容量的函数生成交易区域。 该方法和系统还根据商店位置,商店格式,商店容量,商品类别和相关客户细分,生成代表业务区域中所有客户区域的所有商品类别的所有商店的预期总需求的交易区域需求总和 与贸易区。 构建目标函数作为所述贸易区域需求求和,商店的当前收入以及重新配置商店的实际成本的函数。
    • 8. 发明申请
    • METHOD AND APPARATUS FOR INTEGRATED MULTIPLE FACTORS INTO A UNIFIED OPTIMIZATION MODEL FOR RETAIL NETWORK CONFIGURATION
    • 综合多元因子的方法和装置进入零售网络配置的统一优化模型
    • US20090281869A1
    • 2009-11-12
    • US12117540
    • 2008-05-08
    • Xin Xin BaiJin DongTa-Hsin LiMing XieWen Jun YinBin ZhangCindy Q. Zhang
    • Xin Xin BaiJin DongTa-Hsin LiMing XieWen Jun YinBin ZhangCindy Q. Zhang
    • G06Q10/00
    • G06Q10/04G06Q10/0637
    • A method and system for integrating multiple factors into a unified optimization model for retail network configuration, in one aspect, obtains input data for modeling store configuration. The input data may include demand of each merchandise category from each customer segment in each facility, geographic distribution of stores in an area, current revenue of stores, and physical cost of reconfiguring stores. A trade area is generated as a function of store location, store format, and store capacity. The method and system also generates trade area demand summation representing predicted total demand of all stores for all merchandise categories for all customer segments in the trade area, as a function of store location, store format, store capacity, merchandise category, and customer segment associated with the trade area. An objective function is constructed as a function of said trade area demand summation, current revenue of stores, and physical cost of reconfiguring stores.
    • 一种将多个因素整合到零售网络配置的统一优化模型中的方法和系统,一方面获取用于建模商店配置的输入数据。 输入数据可以包括每个设施中的每个客户段的每个商品类别的需求,区域中的商店的地理分布,商店的当前收入以及重新配置商店的物理成本。 作为商店位置,商店格式和商店容量的函数生成交易区域。 该方法和系统还根据商店位置,商店格式,商店容量,商品类别和相关客户细分,生成代表业务区域中所有客户区域的所有商品类别的所有商店的预期总需求的交易区域需求总和 与贸易区。 构建目标函数作为所述贸易区域需求求和,商店的当前收入以及重新配置商店的实际成本的函数。