区块链网络节点管理软件以账单生成功能为终点的行为路径偏好数据

本数据的应用场景包括：1.软件功能改进和用户体验优化：通过分析区块链网络节点管理软件以账单生成功能为终点的行为路径的用户偏好，有助于开发人员判断软件的用户界面、功能设置、功能布局的合理性及有效性，为本公司团队和其他软件公司（当需开发类似软件时）围绕账单生成功能开展产品优化工作提供依据。2.提升客户支持和服务的针对性：基于以账单生成功能为终点的行为路径的用户偏好数据，可以有针对性地向用户提供技术支持和培训服务，有助于提高用户满意度，减少用户流失，增强用户忠诚度。3.促进软件安全和风险管理：信息安全管理团队可在本数据的基础上，利用其他算法进一步对用户行为路径的异常模式进行监测，从而提前发现潜在的安全威胁。1.数据采集和预处理：（1）从公司自营区块链网络节点管理软件用户行为日志中，实时提取以账单生成功能为终点的用户行为路径数据，包括用户ID、终点功能、行为路径、发生时间。（2）对提取的数据进行清洗，移除无效或错误的记录。
2、计算支持度：（1）基于历史数据，利用数据透视表快速计算所有以账单生成功能为终点的行为路径的总数量。（2）对于以账单生成功能为终点的每种行为路径，基于历史数据利用COUNTIF函数计算其在所有行为路径总数量中出现的次数，形成集合数据X。（3）每种行为路径支持度=每种行为路径出现次数/所有行为路径总数量；形成集合数据Y。
3.频繁路径挖掘：对于每种行为路径（集合数据Y），应用Apriori算法来挖掘频繁出现的行为路径模式（通过设定支持度阈值来提高挖掘的准确性），形成集合数据Z。
4.行为路径偏好识别：根据Apriori算法挖掘出的频繁路径（集合数据Z），结合业务逻辑识别用户偏好的行为路径，即典型行为路径，形成集合数据W。
5.路径排序和输出：按支持度大小对典型行为路径（集合数据W）进行降序排序，并将排序前三的进行可视化输出。

Application scenarios of this dataset include:
1. Software function improvement and user experience optimization: By analyzing user preferences of user behavior paths ending at the bill generation function in the company's self-operated blockchain network node management software, developers can assess the rationality and effectiveness of the software's user interface, function settings, and function layout, providing a basis for our company's team and other software companies (when developing similar software) to carry out product optimization work around the bill generation function.
2. Targeted customer support and service enhancement: Based on user preference data of behavior paths ending at the bill generation function, targeted technical support and training services can be provided to users, which helps to improve user satisfaction, reduce user churn, and strengthen user loyalty.
3. Promotion of software security and risk management: The information security management team can further monitor abnormal patterns of user behavior paths using other algorithms based on this dataset, so as to detect potential security threats in advance.

1. Data collection and preprocessing:
(1) Extract in real-time user behavior path data ending at the bill generation function from the user behavior logs of the company's self-operated blockchain network node management software, including user ID, target function, behavior path, and occurrence time.
(2) Clean the extracted data to remove invalid or erroneous records.

2. Support degree calculation:
(1) Based on historical data, quickly calculate the total number of all behavior paths ending at the bill generation function using a pivot table.
(2) For each type of behavior path ending at the bill generation function, use the COUNTIF function based on historical data to calculate the number of its occurrences in the total number of all behavior paths, forming dataset X.
(3) Support degree of each behavior path = number of occurrences of each behavior path / total number of all behavior paths; form dataset Y.

3. Frequent path mining: For each type of behavior path in dataset Y, apply the Apriori algorithm to mine frequently occurring behavior path patterns (set a support threshold to improve the mining accuracy), forming dataset Z.

4. Behavior path preference identification: According to the frequent paths (dataset Z) mined by the Apriori algorithm, combine with business logic to identify user-preferred behavior paths, namely typical behavior paths, forming dataset W.

5. Path sorting and output: Sort the typical behavior paths (dataset W) in descending order according to their support degrees, and visually output the top three sorted paths.

搜集汇总

数据集介绍

特点

该数据集记录了区块链网络节点管理软件用户以账单生成功能为终点的行为路径偏好，包含978条数据，每日更新。通过Apriori算法挖掘频繁路径和典型行为路径，支持软件功能改进、用户体验优化和客户服务提升。