Data Mining_ Concepts and Techniques - Jiawei Han [366]
■ Fraudulent analysis and the identification of unusual patterns: Fraudulent activity costs the retail industry millions of dollars per year. It is important to (1) identify potentially fraudulent users and their atypical usage patterns; (2) detect attempts to gain fraudulent entry or unauthorized access to individual and organizational accounts; and (3) discover unusual patterns that may need special attention. Many of these patterns can be discovered by multi-dimensional analysis, cluster analysis, and outlier analysis.
As another industry that handles huge amounts of data, the telecommunication industry has quickly evolved from offering local and long-distance telephone services to providing many other comprehensive communication services. These include cellular phone, smart phone, Internet access, email, text messages, images, computer and web data transmissions, and other data traffic. The integration of telecommunication, computer network, Internet, and numerous other means of communication and computing has been under way, changing the face of telecommunications and computing. This has created a great demand for data mining to help understand business dynamics, identify telecommunication patterns, catch fraudulent activities, make better use of resources, and improve service quality.
Data mining tasks in telecommunications share many similarities with those in the retail industry. Common tasks include constructing large-scale data warehouses, performing multi-dimensional visualization, OLAP, and in-depth analysis of trends, customer patterns, and sequential patterns. Such tasks contribute to business improvements, cost reduction, customer retention, fraud analysis, and sharpening the edges of competition. There are many data mining tasks for which customized data mining tools for telecommunication have been flourishing and are expected to play increasingly important roles in business.
Data mining has been popularly used in many other industries, such as insurance, manufacturing, and health care, as well as for the analysis of governmental and institutional administration data. Although each industry has its own characteristic data sets and application demands, they share many common principles and methodologies. Therefore, through effective mining in one industry, we may gain experience and methodologies that can be transferred to other industrial applications.
13.3.3. Data Mining in Science and Engineering
In the past, many scientific data analysis tasks tended to handle relatively small and homogeneous data sets. Such data were typically analyzed using a “formulate hypothesis, build model, and evaluate results” paradigm. In these cases, statistical techniques were typically employed for their analysis (see Section 13.2.1). Massive data collection and storage technologies have recently changed the landscape of scientific data analysis. Today, scientific data can be amassed at much higher speeds and lower costs. This has resulted in the accumulation of huge volumes of high-dimensional data, stream data, and heterogenous data, containing rich spatial and temporal information. Consequently, scientific applications are shifting from the “hypothesize-and-test” paradigm toward a “collect and store data, mine for new hypotheses, confirm with data or experimentation” process. This shift brings about new challenges for data mining.
Vast amounts of data have been collected from scientific domains (including geosciences,