Experiments have been performed using data from the Nokia Mobile Data Challenge (MDC). The results on MDC data show large variability in predictive accuracy of about 17% across users. For example, irregular users are very difficult to predict while for more regular users it is possible to ...
The main purpose of forecasting by data mining in the stock market is to discover knowledge that can assist decision-makers. It is important that companies use data mining with utmost care to improve their business by increasing revenue and reducing costs (Ahmed, 2004). For example, Amazon ...
Classification is one of the supervised learning methods in data mining. The main goal of classification is to connect the input variables with the target variables and make predictions based on this relationship. The classification techniques used in this study ranged from decision tree to support ...
Fig. 1. TOB optimized data matching methodology configured for predicting and data mining highly skewed carbon-flux (net ecosystem carbon exchange; NEE) datasets. TOB Stage 1 assesses data-record matches between records in small, evenly distributed subset designed to tune the algorithm with a larger...
Demonstrating its applicability to microbiome research, SCRuB facilitates improved predictions of host phenotypes, most notably the prediction of treatment response in melanoma patients using decontaminated tumor microbiome data.This is a preview of subscription content, access via your institution ...
Oracle Data Mining Conceptsfor information about predictive data mining. Note: The following example is excerpted from the Data Mining sample programs. For more information about the sample programs, see Appendix A inOracle Data Mining User's Guide. ...
In some cases, it can be important to approximate the range of a target measure of interest instead of computing exact values. For example, selling a stock at a given time can be based on a range containing the target price rather than the exact amount because of the transaction cost. ...
3.2 Illustrative example Considering the \({\mathrm{NO}}_{2}\) Emissions prediction problem described in Sect. 2.2, the Directive 2008/50/EC contains information on the relevance of certain data points. In particular, the goal to maintain the LNO2 hourly concentration values below a limit equ...
Two hyperparameters are needed for the SVM algorithm: cost (C), which indicates the degree of penalty for misclassification, and gamma (γ), which defines the extent of the influence of a single training example. In this study, we adopted the Gaussian radial basis kernel for SVM. The “...
The most important risk factors have been recognized and used to build forecasting models including spatiotemporal visualisation, time-series, and data mining models. These models could facilitate the process of decision making by predicting the time and location of epidemics. In addition, social ...