data mining - analysis
Data Mining and Machine Learning - Towards Reducing False Positives in Intrusion Detection
White Paper This paper presents two orthogonal and complementary approaches to reduce the number of false positives in intrusion detection using alert postprocessing by data mining and machine learning. Intrusion... [01 Oct 2008]
Data Mining Challenges for Electronic Safety: The Case of Fraudulent Intent Detection in E-Mails
White Paper This paper introduces an array of challenges which are ripe for the attention of the data mining research community and are vastly different from those of combating the general problem of spam. They... [07 Oct 2008]
Data Mining and Complex Telecommunications Problems Modeling
White Paper This paper focuses on using data mining for solving selected problems in telecommunication industry. Moreover, the new technological developments, the convergence of voice and data... [07 Oct 2008]
Data Mining in the Insurance Industry: Solving Business Problems Using SAS Enterprise Miner Software
White Paper Data mining can be defined as the process of selecting, exploring and modeling large amounts of data to uncover previously unknown patterns. In the insurance industry,... [02 Sep 2009]
Data Mining in Telecommunications
White Paper This paper describes how data mining can be used to uncover useful information buried within these data sets. Several data mining applications are... [19 Sep 2008]
Data Mining and Bioinformatics
White Paper This paper looks at the use of Data Mining in the domain of Bioinformatics. The broad use of data mining and their applicability in the different areas of bioinformatics... [19 Sep 2008]
Data Mining, National Security, Privacy and Civil Liberties
White Paper There has been much interest recently on using data mining for counter-terrorism applications. For example, data mining can be used to detect unusual patterns, terrorist... [19 Sep 2008]
Data Mining in Social Networks
White Paper Recent research projects in two closely related areas of computer science - machine learning and data mining - have developed methods for constructing statistical models of network data.... [19 Sep 2008]
Data Mining Within DBMS Functionality
White Paper Data mining slowly evolves from simple discovery of frequent patterns and regularities in large data sets toward interactive, user-oriented, on-demand decision supporting. Since... [01 Oct 2008]
Data Mining With Cellular Automata
White Paper This paper explores the use of cellular automata for data mining, specifically for classification tasks. A cellular automaton is a discrete, dynamical system composed of very simple, uniformly... [01 Oct 2008]
Data Mining Model Management to Support Real-Time Business Intelligence in Service-Oriented Architectures
White Paper However, serious challenges remain on managing data mining models and integrating them with business services using Service-Oriented Architectures (SOA) to provide real-time Business Intelligence (BI). [29 Aug 2009]
Data Mining in Chemometrics: Sub-Structures Learning Via Peak Combinations Searching in Mass Spectra
White Paper Data Mining (DM), also named Knowledge Discovery in Databases (KDD) is an extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from... [01 Oct 2008]
Data Mining in the Real World: Experiences, Challenges, and Recommendations
White Paper Data mining is used regularly in a variety of industries and is continuing to gain in both popularity and acceptance. However, applying data mining methods to complex... [29 Aug 2009]
Data Mining and Audience Intelligence for Advertising
White Paper Growth in the global advertising industry - especially the recent rapid growth in online advertising - has generated large volumes of data, bringing along with it many challenging data... [19 Sep 2008]
Data Mining Diabetic Databases: Are Rough Sets a Useful Addition?
White Paper The publicly available Pima Indian Diabetic Database (PIDD) at the UC-Irvine Machine Learning Lab has become a standard for testing data mining algorithms to see their accuracy in predicting diabetic... [01 Oct 2008]
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