【正文】 rary of CongressData Mining: An OverviewSummaryData mining is emerging as one of the key features of many homeland security initiatives. Often used as a means for detecting fraud, assessing risk, and product retailing, data mining involves the use of data analysis tools to discover previously unknown, valid patterns and relationships in large data sets. In the context of homeland security, data mining is often viewed as a potential means to identify terrorist activities, such as money transfers and munications, and to identify and track individual terrorists themselves, such as through travel and immigration records.While data mining represents a significant advance in the type of analytical tools currently available, there are limitations to its capability. One limitation is that although data mining can help reveal patterns and relationships, it does not tell the user the value or significance of these patterns. These types of determinations must be made by the user. A second limitation is that while data mining can identify connections between behaviors and/or variables, it does not necessarily identify a causal relationship. To be successful, data mining still requires skilled technical and analytical specialists who can structure the analysis and interpret the output that is created.Data mining is being increasingly mon in both the private and public sectors. Industries such as banking, insurance, medicine, and retailing monly use data mining to reduce costs, enhance research, and increase sales. In the public sector, data mining applications initially were used as a means to detect fraud and waste, but have grown to also be used for purposes such as measuring and improving program performance. However, some of the homeland security data mining applications represent a significant expansion in the quantity and scope of data to be analyzed. Two efforts that have attracted a higher level of congressional interest include the Terrorism Information Awareness (TIA) project (nowdiscontinued) and the ComputerAssisted Passenger Prescreening System II (CAPPS II ) project (nowcanceled and replaced by Secure Flight).As with other aspects of data mining, while technological capabilities are important, there are other implementation and oversight issues that can influence the success of a project’s oute. One issue is data quality, which refers to the accuracy and pleteness of the data being analyzed. A second issue is the interoperability of the data mining software and databases being used by different agencies. A third issue is mission creep, or the use of data for purposes other than for which the data were originally collected. A fourth issue is privacy. Questions that may be considered include the degree to which government agencies should use and mix mercial data with government data, whether data sources are being used for purposes other than those for which they were originally designed, and possible application of the Privacy Act to these initiatives. It is anticipated that congressional oversight of data mining projects will grow as data mining efforts continue to evolve. This report will be updated as events warrant.Contents What is Data Mining? 33 Limitations of Data Mining 35Data Mining Uses 35Terrorism Information Awareness (TIA) Program 37ComputerAssisted Passenger Prescreening System 39Data Mining Issues 43Data Quality 43Interoperability 43Mission Creep 44Privacy 45Legislation in the 108th Congress 45For Further Reading 48 Data Ming: An Overview What is Data Mining?Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data These tools can include statistical models, mathematical algorithms, and machine learning methods (algorithms that improve their performance automatically through experience, such as neural networks or decision trees). Consequently, data mining consists of more than collecting and managing data, it also includes analysis and prediction. Data mining can be performed on data represented in quantitative, textual, or multimedia forms. Data mining applications can use a variety of parameters to examine the data. They include association (patterns where one event is connected to another event, such as purchasing a pen and purchasing paper), sequence or path analysis (patterns where one event leads to another event, such as the birth of a child and purchasing diapers), classification (identification of new patterns, such as coincidences between duct tape purchases and plastic sheeting purchases), clustering (finding and visually documenting groups of previously unknown facts, such as geographic location and brand preferences), and forecasting (discovering patterns from which one can make reasonable predictions regarding future activities, such as the prediction that people who join an athletic club may take exercise classes).2 As an application, pared to other data analysis applications, such as structured queries (used in many mercial databases) or statistical analysis software, data mining represents a difference of kind rather than degree. Many simpler analytical tools utilize a verificationbased approach, where the user develops a hypothesis and then tests the data to prove or disprove the hypothesis. For example, a user might hypothesize that a customer who buys a hammer, will also buy a box of nails. The effectiveness of this approach can be limited by the creativity of the user to develop various hypotheses, as well as the structure of the software being used. In contrast, data mining utilizes a discovery approach, in which algorithms can be used to examine several multidimensional data relationships simultaneously, identifying those that are unique or frequently represented. For example, a hardware store may