Advanced Techniques in Knowledge Discovery and Data Mining
Springer Science & Business Media, Dec 31, 2007 - Computers - 256 pages
Data mining and knowledge discovery (DMKD) is a rapidly expanding field in computer science. It has become very important because of an increased demand for methodologies and tools that can help the analysis and understanding of huge amounts of data generated on a daily basis by institutions like hospitals, research laboratories, banks, insurance companies, and retail stores and by Internet users. This explosion is a result of the growing use of electronic media. But what is data mining (DM)? A Web search using the Google search engine retrieves many (really many) definitions of data mining. We include here a few interesting ones. One of the simpler definitions is: “As the term suggests, data mining is the analysis of data to establish relationships and identify patterns” . It focuses on identifying relations in data. Our next example is more elaborate: An information extraction activity whose goal is to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis .
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accuracy agents analysis applications approach attributes Bayesian networks CCGA chromosomes classification Clusion clustering CNFM conditional independence crossover data mining data set defined denotes detection dimensionality reduction Discovery and Data DM tools DMKD process domain evaluation Evolutionary Algorithms Evolutionary Computation example experts extracted feature selection feature space function genetic algorithms graph hidden neurons high-dimensional hyperspheres IEEE implementation input Instance Selection interactive International Conference Knowledge Discovery Kohonen’s layer learning algorithm Machine Learning mapping matrix MDLEP medical databases methods Mining and Knowledge mutation nearest neighbor negative rules network structure neural fuzzy network neural networks neuron node OLAP optimal output parameters parent set partitioning patterns performance PMML population positive rules problem Proc rough sets samples Satimage Section segmentation self-organizing map semiconductor similarity similarity matrix solution SONFIN split statistical step subset Table techniques technologies training set UDDI variables vectors visualization wafer weight XML-RPC
Page vii - Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results.