Knowledge Discovery and Data Mining: theory and practice
Scope: Knowledge discovery has been defined as 'the extraction of implicit, previously unknown and potentially useful information from data'. In a world increasingly overloaded with data of varying quality, not least via the Internet, computerised tools are becoming useful to "mine" useful data from the mass available. This has led to data mining becoming an important aspect of IT and applied computing. This book reviews some of the underlying technologies and also some recent applications in a number of fields.
Researchers in applied computing; IT professionals working in a wide variety of fields, such as meteorology, health, biochemistry and engineering.
1: Estimating concept difficulty with cross entropy (K. Nazar & M.A. Bramer);
2: Analysing outliers by searching for plausible hypotheses (X. Liu & G. Cheng);
3: Attribute-value distribution as a technique for increasing the efficiency of data mining (D. McSherry);
4: Using background knowledge with attribute-oriented data mining (M. Shapcott, S. McClean and B. Scotney);
5: A development framework for temporal data mining (X. Chen & I. Petrounias);
6: An integrated architecture for OLAP and data mining (Z. Chen);
7: Empirical studies of the knowledge discovery approach to health-information analysis (M. Lloyd-Williams);
8: Direct knowledge discovery and interpretation from a multilayer perceptron network which performs low-back pain classification (M.L. Vaughn, S.J. Cavill, S.J. Taylor, M.A. Foy and A.J.B. Fogg);
9: Discovering knowledge from low quality meteorological databases (C.M. Howard & V.J. Rayward-Smith);
10: A meteorological knowledge-discovery environment (A.G. Büchner, J.C.L. Chan, S.L. Hung and J.G. Hughes);
11: Mining the organic compound jungle a functional programming approach (K.E. Burn-Thornton & J. Bradshaw);
12: Data mining with neural networks an applied example in understanding electricity consumption patterns (P. Brierley & B. Batty); Index.