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.
Data mining is the process of uncovering patterns, associations, anomalies, and statistically significant structures and events in data. It borrows and builds on ideas from many disciplines, ranging from statistics to machine learning, mathematical optimization, and signal and image processing. Data mining techniques are becoming an integral part of scientific endeavors in many application domains, including astronomy, bioinformatics, chemistry, materials science, climate, fusion, and combustion. In this chapter, we provide a brief introduction to the data mining process and some of the algorithms used in extracting information from scientific data sets.
The third SIAM International Conference on Data Mining provided an open forum for the presentation, discussion and development of innovative algorithms, software and theories for data mining applications and data intensive computation. This volume includes 21 research papers.
We are very pleased to present the proceedings of the 2003 SIAM International Conference on Data Mining. The field of Data Mining has seen a tremendous increase of interest in recent months. Applications of Data Mining are mentioned often in the daily press, especially in the fields of security and forensics. Thus, these are exciting times for researchers and practitioners in the area. We hope that the research captured by these proceedings helps in advancing this important field.
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.
Data mining is the process of uncovering patterns, associations, anomalies, and statistically significant structures and events in data. It borrows and builds on ideas from many disciplines, ranging from statistics to machine learning, mathematical optimization, and signal and image processing. Data mining techniques are becoming an integral part of scientific endeavors in many application domains, including astronomy, bioinformatics, chemistry, materials science, climate, fusion, and combustion. In this chapter, we provide a brief introduction to the data mining process and some of the algorithms used in extracting information from scientific data sets.
Presenting a communicational perspective on the British empire in India during the 20th century, the book seeks to examine how, and explain why, British proconsuls, civil servants and even the monarch George V, as well as Indian nationalists, interacted with the media, primarily British and American, and with what consequences.
Nisha’s world falls apart when she learns that her grandmother is hiding her from her father and uncle. Nisha is against running away and hiding. With the help of her friends, she is ready to face them and make them pay for the murder of her mother and grandfather. They plan to expose these ruthless people and put them where they belong—behind bars.
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