Biomedical Engineering Online Review of "data Mining: Practical Machine Learning Tools and Techniques" by Witten and Frank Book Details
Biomedical Engineering Online Review of "data Mining: Practical Machine Learning Tools and Techniques" by Witten and Frank Book Details
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作者：
F. Azuaje
在1990年代初期，计算机科学界的一些部门正在发展数据理解作为发现驱动，系统和迭代过程的思想。预计该“数据挖掘”研发领域将利用机器学习方法的扩展和巩固以及传统统计分析和数据库管理策略的整合。主要目标是确定大型数据集中的相关，有趣且潜在的新型信息模式和关系，以支持决策和知识发现。在1990年代中期数据挖掘应用程序的价值。在接下来的几年中，国际会议，期刊和书籍更频繁地报告其他领域的进步，工具和应用，例如生物医学信息，工程，物理，执法和农业。如今，数据挖掘被视为一种学科或范式，积极地有助于这些科学领域的发展（例如，基于Web的计算和系统生物学）。数据挖掘已成为医疗保健和生物医学计算应用进展方面的基本研究主题。数据挖掘的进步在医疗组织的信息管理，消费者健康信息学，公共卫生和流行病学，患者护理和监测系统，大规模图像分析到科学文献的信息提取和分类[1]等领域具有应用和影响[1]。与数据挖掘相关的方法，技术和应用也显着支持了生物信号处理中的不同数据理解和决策支持任务，例如分类，可视化和鉴定诊断变量或患者组之间的复杂关系[2,3]。 Witten和Frank在“数据挖掘：实用的机器学习工具和技术”中，为用户，学生和研究人员提供了对设计，实施和评估数据挖掘应用程序的概念，技术和工具的平衡，清晰的介绍。尽管它强调机器学习技术，但也引入了基本统计和信息表示方法。本书提供了各种简单而优雅的解释，以指导读者了解基本概念和方法。这本书也可以看作是结构良好，密集的教程，它在解释如何实施解决不同问题的解决方案方面表现出色。
In the early 1990s some sectors of the computer science community were developing the idea of data understanding as a discovery-driven, systematic and iterative process. This "data mining" research and development area was expected to take advantage of the expansion and consolidation of machine learning methodologies together with the integration of traditional statistical analysis and database management strategies. The main goal was to identify relevant, interesting and potentially novel informational patterns and relationships in large data sets to support decision making and knowledge discovery. In the mid 1990s developers and users of decision-making support systems in areas such as finance (e.g. credit approval and fraud detection applications), marketing and sales analysis (e.g. shopping patterns and sales prediction) were showing a great deal of enthusiasm about the business value of data mining applications. During the next few years international conferences, journals and books were more frequently reporting advances, tools and applications in other areas such as biomedical informat-ics, engineering, physics, law enforcement and agriculture. Today data mining is seen as a discipline or paradigm that actively aids in the development of these and other scientific areas (e.g. Web-based computing and systems biology). Data mining has become a fundamental research topic in the progression of computing applications in health care and biomedicine. Advances in data mining have applications and implications in areas ranging from information management in healthcare organisations, consumer health informatics, public health and epidemiology, patient care and monitoring systems, large-scale image analysis to information extraction and classification of scientific literature [1]. Approaches, techniques and applications associated with data mining has also significantly supported different data understanding and decision support tasks in bio-signal processing, such as the classification , visualisation and identification of complex relationships between diagnostic variables or groups of patients [2,3]. In "Data Mining: Practical Machine Learning Tools and Tech-niques" Witten and Frank offer users, students and researchers alike a balanced, clear introduction to concepts , techniques and tools for designing, implementing and evaluating data mining applications. Although it puts emphasis on machine learning techniques, it also introduces basic statistical and information representation methods. This book provides a variety of simple yet elegant explanations to guide the reader to understand essential concepts and approaches. The book can also be seen as a well-structured, intensive tutorial, which excels in explaining how to implement solutions to different problems .