Volumes Of Data For Patterns Research Articles

Applying data mining for the analysis of breast cancer data.

Data mining, also known as Knowledge-Discovery in Databases (KDD), is the process of automatically searching large volumes of data for patterns. For instance, a clinical pattern might indicate a female who have diabetes or hypertension are easier suffered from stroke for 5 years in a future. Then, a physician can learn valuable knowledge from the data mining processes. Here, we present a study focused on the investigation of the application of artificial intelligence and data mining techniques to the prediction models of breast cancer. The artificial neural network, decision tree, logistic regression, and genetic algorithm were used for the comparative studies and the accuracy and positive predictive value of each algorithm were used as the evaluation indicators. 699 records acquired from the breast cancer patients at the University of Wisconsin, nine predictor variables, and one outcome variable were incorporated for the data analysis followed by the tenfold cross-validation. The results revealed that the accuracies of logistic regression model were 0.9434 (sensitivity 0.9716 and specificity 0.9482), the decision tree model 0.9434 (sensitivity 0.9615, specificity 0.9105), the neural network model 0.9502 (sensitivity 0.9628, specificity 0.9273), and the genetic algorithm model 0.9878 (sensitivity 1, specificity 0.9802). The accuracy of the genetic algorithm was significantly higher than the average predicted accuracy of 0.9612. The predicted outcome of the logistic regression model was higher than that of the neural network model but no significant difference was observed. The average predicted accuracy of the decision tree model was 0.9435 which was the lowest of all four predictive models. The standard deviation of the tenfold cross-validation was rather unreliable. This study indicated that the genetic algorithm model yielded better results than other data mining models for the analysis of the data of breast cancer patients in terms of the overall accuracy of the patient classification, the expression and complexity of the classification rule. The results showed that the genetic algorithm described in the present study was able to produce accurate results in the classification of breast cancer data and the classification rule identified was more acceptable and comprehensible.

New Patterns And Techniques In Knowledge Discovery Through Data Mining

The main focus of this paper discussion was on mining and its set of techniques used in an automated approach to exhaustively explore and bring to the surface complex relationships in very large datasets. We discussed how a decision support process can be used to search for patterns of information in data. And also discussed different techniques for finding and describing structural patterns in data as well. Knowledge Discovery is a concept that describes the process of automatically searching large volumes of data for patterns that can be considered knowledge about the data. We discussed all automatic and semi-automatic process of discovering the patterns in data that how it leads to some advantage in businesses to make knowledge driven decisions, which help the company to succeed and compete.

Guest Editorial Introduction to the Special Section on Mining Biomedical Data

Research in data mining, also known as knowledge discovery in databases, aims at developing intelligent tools to automatically search large volumes of data for patterns. Biomedicine is a data-intensive field that forms one of the most important application domains in data mining and knowledge discovery. This special section contains 15 extended papers, focusing on mining of biomedical data, presented at the 18th IEEE International Symposium on Computer-Based Medical Systems, IEEE CBMS 2005, which was held on June 23–24, 2005, at Trinity College, Dublin, Ireland. This editorial first defines the field and then introduces the papers.

Efficiency of electron-beam proximity effect correction

The trend of increasing pattern data volume, together with decreasing critical dimensions, causes correction of proximity effect to be continually more time consuming and expensive. A tradeoff exists between speed and accuracy. A given correction method may be judged efficient if the direct computational overhead needed to achieve a predetermined level of accuracy is small. Some basic principles relating to efficiency are derived. An optimal correction algorithm follows naturally from the analysis. Forward and backward scattering have greatly differing ranges, and correspondingly differing computational requirements. The two processes are easily separated analytically. Dose correction is used to offset backscattering, while edge displacement is used to offset forward scattering. The only direct computational overhead is the local pattern density. All other necessary parameters are precomputed, and accessed by memory lookup.