By Kenneth D. Lawrence, Stephan Kudyba, Ronald K. Klimberg
With today’s details explosion, many businesses at the moment are in a position to entry a wealth of priceless facts. regrettably, every one of these enterprises locate they're ill-equipped to arrange this knowledge, not to mention placed it to paintings for them.
Gain a aggressive virtue
- Employ info mining in learn and forecasting
- Build types with information administration instruments and technique optimization
- Gain subtle breakdowns and complicated research via multivariate, evolutionary, and neural web methods
- Learn the way to classify information and continue quality
Transform info into enterprise Acumen
Data Mining tools and Applications offers businesses with the knowledge administration instruments that would let them harness the severe proof and figures had to increase their base line. Drawing from finance, advertising, economics, technological know-how, and healthcare, this ahead pondering volume:
- Demonstrates how the transformation of knowledge into enterprise intelligence is a vital point of strategic decision-making
- Emphasizes using information mining options in real-world situations with huge database parts
- Focuses on info mining and forecasting equipment in engaging in marketplace research
Read or Download Data Mining Methods and Applications PDF
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Additional resources for Data Mining Methods and Applications
4. Does the training sample size affect the relative performance of the neural network ensemble strategy? Training examples are scarce in many business and health-care applications; there is never too much data. In this research we study training set sizes ranging from a few hundred to a thousand observations. indd 40 11/15/07 1:34:24 AM Ensemble Strategies for Neural Network Classifiers n 41 5. Does missing information affect the relative performance of neural network ensemble strategy? Missing information may result from the physical inability to collect certain information or from not appreciating the significance of some information sources.
The generalization error of these strategies is compared at controlled levels of design and exogenous variables that influence the classification problem. These variables include classification complexity, training set size, missing information, and data imperfections that include irrelevant information and mislabeled training examples. 3 Research Methodology We investigate the relative performance of MLP ensembles used in classification applications where the data conditions can be characterized as two naturally occurring clusters.
1 Generation of Datasets This research focuses on data that has the spatial characteristics of two naturally occurring clusters. The definition of a cluster structure is an important operational consideration in the generation of artificial datasets. We define cluster structure around the properties of external isolation and internal cohesion . This definition requires that members of one cluster be separated from members of all other clusters by fairly empty areas of space. Internal cohesion requires that entities within the same cluster should be similar to each other.