Data Mining_ Concepts and Techniques - Jiawei Han [360]
Web content mining has been studied extensively by researchers, search engines, and other web service companies. Web content mining can build links across multiple web pages for individuals; therefore, it has the potential to inappropriately disclose personal information. Studies on privacy-preserving data mining address this concern through the development of techniques to protect personal privacy on the Web.
Web structure mining is the process of using graph and network mining theory and methods to analyze the nodes and connection structures on the Web. It extracts patterns from hyperlinks, where a hyperlink is a structural component that connects a web page to another location. It can also mine the document structure within a page (e.g., analyze the treelike structure of page structures to describe HTML or XML tag usage). Both kinds of web structure mining help us understand web contents and may also help transform web contents into relatively structured data sets.
Web usage mining is the process of extracting useful information (e.g., user click streams) from server logs. It finds patterns related to general or particular groups of users; understands users' search patterns, trends, and associations; and predicts what users are looking for on the Internet. It helps improve search efficiency and effectiveness, as well as promotes products or related information to different groups of users at the right time. Web search companies routinely conduct web usage mining to improve their quality of service.
Mining Data Streams
Stream data refer to data that flow into a system in vast volumes, change dynamically, are possibly infinite, and contain multidimensional features. Such data cannot be stored in traditional database systems. Moreover, most systems may only be able to read the stream once in sequential order. This poses great challenges for the effective mining of stream data. Substantial research has led to progress in the development of efficient methods for mining data streams, in the areas of mining frequent and sequential patterns, multidimensional analysis (e.g., the construction of stream cubes), classification, clustering, outlier analysis, and the online detection of rare events in data streams. The general philosophy is to develop single-scan or a-few-scan algorithms using limited computing and storage capabilities.
This includes collecting information about stream data in sliding windows or tilted time windows (where the most recent data are registered at the finest granularity and the more distant data are registered at a coarser granularity), and exploring techniques like microclustering, limited aggregation, and approximation. Many applications of stream data mining can be explored—for example, real-time detection of anomalies in computer network traffic, botnets, text streams, video streams, power-grid flows, web searches, sensor networks, and cyber-physical systems.
13.2. Other Methodologies of Data Mining
Due to the broad scope of data mining and the large variety of data mining methodologies, not all methodologies of data mining can be thoroughly covered in this book. In this section, we briefly discuss several interesting methodologies that were not fully addressed in the previous chapters. These methodologies are listed in Figure 13.3.
Figure 13.3 Other data mining methodologies.
13.2.1. Statistical Data Mining
The data mining techniques described in this book are primarily drawn from computer science disciplines, including data mining, machine learning, data warehousing, and algorithms. They are designed for the efficient handling of huge amounts of data that are typically multidimensional and possibly of various complex types. There are, however, many well-established statistical techniques for data analysis, particularly for numeric data. These techniques have been applied extensively