Description: 基于weka的分类算法，用于weka拓展应用。朴素贝叶斯模型发源于古典数学理论，有着坚实的数学基础，以及稳定的分类效率。同时，该算法所需估计的参数很少，对缺失数据不太敏感，算法也比较简单。理论上，与其他分类方法相比具有最小的误差率。-Based on the classification algorithm weka, weka develop applications for. Naive Bayes model originated in the classical mathematical theory, has a solid mathematical basis, as well as the stability of the classification efficiency. At the same time, the algorithm estimates the parameters required for small, less sensitive to missing data, the algorithm is also relatively simple. In theory, when compared with other classification methods with the smallest margin of error. Platform: |
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Author:zhangrui |
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Description: DIGGER社交网络数据挖掘分析系统，本系统的挖掘工作是在WEKA平台下进行的，WEKA作为一个公开的数据挖掘工作平台，集合了大量能承担数据挖掘任务的机器学习算法，包括对数据进行预处理，分类，回归、聚类、关联规则以及在新的交互式界面上的可视化。DIGGER作为一个社交网络系统，它利用数据挖掘工具WEKA在大规模海量数据中建立模型和发现数据间关系，这些模型和关系可以用来做出决策和预测。
第一章描述了开题的背景和需求，第二章描述该系统的关键技术和开发环境，第三章是系统的设计，第四章对框架的研究和系统中框架的运用，第五章是对系统安全性的研究以及在系统中的实现，第六章对系统进行测试。第七章就本次的设计进行了总结。-DIGGER social network data mining analysis system, the system of the excavation work was carried out in the WEKA platform, WEKA data mining as an open working platform, a collection of a lot to take on the task of data mining machine learning algorithms, including preprocessing of the data , classification, regression, clustering, association rules, and in the new interactive interface visualization. DIGGER as a social networking system that uses data mining tool WEKA in large amounts of data to establish the relationship between the data model and the discovery of these models and relationships can be used to make decisions and predictions. The first chapter describes the background and needs of the opening title, the second chapter describes the key technologies of the system and development environment, and the third chapter is the system design, the fourth chapter of the framework of research and the use of the framework of the system, the fifth chapter is Research on system securi Platform: |
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Author:萦婧青心 |
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