Chandrika Kamath describes how techniques from the multi-disciplinary field of data mining can be used to address the modern problem of data overload in science and engineering domains. Starting with a survey of analysis problems in different applications, it identifies the common themes across these domains.
Federal Efforts Cover a Wide Range of Uses : Report to the Ranking Minority Member, Subcomittee on Financial Management, the Budget, and International Security, Committee on Governmental Affairs, U.S. Senate
Data mining is becoming a big business; Forrester Research has estimated that
the data mining market is passing the billion dollar mark. Although the use and
sophistication of data mining have increased in both the government and the ...
Yielding substantial knowledge from data primarily gathered for a wide range of quite different applications, data mining is a promising and relatively new area of current research and development. This book features papers from the Fifth International Conference on Data Mining. Text Mining and Their Business Applications. Sharing state-of-the-art results and practical development experiences, these allow researchers and applications developers from a variety of areas to learn about the many different applications of data mining and how the techniques can help in their own field. The volume features contributions on topics such as: Data Preparation - Data Selection; Preprocessing; Transformation. Techniques - Neural Networks; Decision Trees; Genetic Algorithms; Information Extraction; Clustering; Categorization. Special Applications - Customer Relationship Management; Competitive Intelligence.
Besides, from Web Mining, new research areas were derived to guide the
solutions to its specific needs. In short, some researchers have worked on mining
the content of a web site (web content mining), others have decided to study the ...
"This collection offers tools, designs, and outcomes of the utilization of data mining and warehousing technologies, such as algorithms, concept lattices, multidimensional data, and online analytical processing. With more than 300 chapters contributed by over 575 experts from around the globe, this authoritative collection will provide libraries with the essential reference on data mining and warehousing"--Provided by publisher.
"This collection offers tools, designs, and outcomes of the utilization of data mining and warehousing technologies, such as algorithms, concept lattices, multidimensional data, and online analytical processing.
The Fourth SIAM International Conference on Data Mining continues the tradition of providing an open forum for the presentation and discussion of innovative algorithms as well as novel applications of data mining. This is reflected in the talks by the four keynote speakers who discuss data usability issues in systems for data mining in science and engineering, issues raised by new technologies that generate biological data, ways to find complex structured patterns in linked data, and advances in Bayesian inference techniques. This proceedings includes 61 research papers.
The Fifth SIAM International Conference on Data Mining continues the tradition of providing an open forum for the presentation and discussion of innovative algorithms as well as novel applications of data mining. Advances in information technology and data collection methods have led to the availability of large data sets in commercial enterprises and in a wide variety of scientific and engineering disciplines. The field of data mining draws upon extensive work in areas such as statistics, machine learning, pattern recognition, databases, and high performance computing to discover interesting and previously unknown information in data. This conference results in data mining, including applications, algorithms, software, and systems.
In addition, WFIM can generate orders of magnitude fewer patterns than the
traditional weighted frequent itemset mining algorithms. To test the scalability
with the number of transactions, the TlOMDxK dataset was used. WFIM scales
Automatically analyzing video data is extremely important for applications such as monitoring and data collection in transportation scenarios. Machine learning techniques are often employed in order to achieve these goals of mining traffic video to find interesting events. Typically, learning-based methods require significant amount of training data provided via human annotation. For instance, in order to provide training, a user can give the system images of a certain vehicle along with its respective annotation. The system then learns how to identify vehicles in the future - however, such systems usually need large amounts of training data and thereby cumbersome human effort. In this research, we propose a method for active l\earning in which the system interactively queries the human for annotation on the most informative instances. In this way, learning can be accomplished with lesser user effort without compromising performance. Our system is also efficient computationally, thus being feasible in real data mining tasks for traffic video sequences.
Machine learning techniques are often employed in order to achieve these goals of mining traffic video to find interesting events. Typically, learning-based methods require significant amount of training data provided via human annotation.