Small OCR Application Supported by Data Mining Algorithms

数据挖掘——概念、模型、方法和算法。PDF版本，国外经典教材，清华大学出版社出版。

数据挖掘中的10大算法抽象的介绍参考：1. 数据挖掘的10大算法2. ICDM 06关于“数据挖掘中数据挖掘的10大算法”的小组讨论3. 数据挖掘的10大算法4. 数据挖掘前10大算法的18个候选算法5. T-61.6020计算机与信息科学专题课程II P：数据挖掘和机器学习中的流行算法6. IEEE数据挖掘国际会议

数据挖掘——概念、模型、方法和算法 DATA MINING Concepts，Models，Methods，and Algorithms（美）Mehmed Kantardzic著，闪四清、陈茵程、雁等译，清华大学出版社

In today's digital era, data mining has emerged as a vital tool for extracting valuable knowledge from large datasets across various industries. This is particularly crucial in resource-based sectors like coal mining, where data mining plays an essential role in improving safety, operational efficiency, and decision-making. Data mining refers to the process of applying algorithms to identify patterns, correlations, and trends within large datasets. In the context of coal mining enterprises, this technology can analyze historical data to predict potential hazards, such as gas explosions or water inflow incidents, thereby enhancing mine safety. In terms of production optimization, data mining helps companies improve coal yield and resource utilization by identifying key factors influencing output. By analyzing data from various stages of production, mining companies can devise more efficient strategies, boosting production and reducing costs, thereby increasing profitability. Additionally, data mining has wide-ranging applications in other aspects of coal mine management, including human resource management and equipment maintenance. By analyzing employee performance data, it helps in identifying high-performing individuals, guiding talent management and development. In equipment maintenance, data mining allows for predictive analytics, identifying potential failures before they occur, thus reducing downtime and minimizing operational disruptions. Despite its numerous benefits, the practical implementation of data mining in coal mining enterprises faces several challenges, such as data quality control, model selection, and result interpretation. Data quality is crucial to ensuring accurate and reliable results, necessitating robust data management systems. Moreover, the selection and optimization of mining-specific algorithms require continuous refinement to achieve optimal outcomes. The ultimate goal of data mining is to translate analytical results into actionable insights, requiring companies to develop mechanisms for quick and accurate decision-making. In summary, the application of data mining in coal mining enterprises is a complex system requiring a strategic approach. It involves preparing for organizational changes, upgrading technical capabilities, and fostering talent and cultural development. As technology continues to advance, the role of data mining will undoubtedly expand, offering new opportunities for efficiency and safety in the mining industry.

本书重点介绍了用于解决数据挖掘中关键问题的实用算法，甚至可以在最大的数据集上使用这些算法。

ANDAS is a Java-based web application that provides a convenient way for users to process and analyze their datasets, particularly through sorting and data mining techniques. In this system, tf-idf (term frequency-inverse document frequency) is a crucial algorithm used to measure the importance of specific words in documents. The development language of ANDAS, Java, is renowned for its cross-platform capability and robust library support. Its strong typing ensures code stability and security, enabling ANDAS to deliver reliable services. JavaServer Faces (JSF) in the Java EE ecosystem provides UI component framework for building dynamic, interactive web applications in a declarative manner, enhancing ANDAS's user interface for intuitive data handling and display. XML (eXtensible Markup Language) is employed in ANDAS for data exchange and storage, organizing data in a structured format that facilitates parsing and sharing from diverse sources. JBoss, an open-source Java EE application server, chosen for its stability and scalability, supports ANDAS for web application runtime. Enterprise JavaBeans (EJB), integral to Java EE, empowers ANDAS with services like transaction management, security, and persistence, handling complex data operations and concurrency issues. H2, a lightweight relational database management system, likely used as backend storage in ANDAS, ensures efficient performance and easy integration for small-scale web applications. AJAX (Asynchronous JavaScript and XML) facilitates interactive web app features in ANDAS, enabling asynchronous data loading and user interaction enhancements such as real-time feedback during data filtering or sorting. ANDAS integrates Java, JSF, XML, JBoss, EJB, H2, and AJAX technologies to efficiently and stably handle user datasets, utilizing algorithms like tf-idf to reveal data insights.

回归分析的基本原理 所谓回归分析法，利用数理统计方法建立因变量与自变量之间的回归关系函数表达式。对于下表中的数据：利用回归分析方法，得到如下的函数关系式：

Oracle数据库系统支持多种数据类型，用于定义表中的列以存储不同形式的信息。以下是主要数据类型： 字符类： CHAR：定长字符串类型，最大2000字符，如CHAR(10)分配10个字符空间。 VARCHAR2：变长字符串类型，最大4000字符，只分配实际需要的字符数。 大对象类型： CLOB：存储大量文本数据，最大4GB，适合长文档。 数字型： NUMBER：通用数值类型，存储整数或浮点数，范围从-10^38到10^38。 日期和时间类型： DATE：存储日期和时间，精确到天； TIMESTAMP：更精确的时间类型，精确到毫秒。 二进制数据类型： BLOB：存储二进制大数据（如图片、音频文件），最大4GB。选择合适的数据类型能优化查询性能并降低存储成本。

数据挖掘原理是指从大量的数据中提取有价值的信息和知识的过程。这个过程通常包括数据的清洗、集成、选择、变换、挖掘和评估等多个步骤。通过运用统计学、机器学习和数据库系统等技术，数据挖掘能够识别数据中的模式和关系，为决策提供支持。