Prediction of protein interactions is one of the central problems in post–genomic biology. In this paper, we present an association rule-based protein interaction prediction method. We adopted neural network to cluster protein interaction data, and used information theory based feature selection method to reduce protein feature dimension. After model training, feature association rules are gene...

In this paper, we present an association rule based protein interaction prediction method. We use neural network to cluster protein interaction data and feature selection method to reduce protein feature dimension. After this model training, association rules for protein interaction prediction are generated by decoding a set of learned weights of trained neural network and association rule mini...

Text classification approach gaining more importance because of the accessibility of large number of electronic documents from a variety of resource. Text categorization (Also called Text Categorization) is the task of assigning predefined categories to documents. It is the method of finding interesting regularities in large textual, where interesting means non trivial, hidden, previously unkno...

Data mining offers an effective solution to product and process diagnosis in that it requires no additional equipment or capital investment and it does not cause interruption to production and operations. The most important role of data mining is the ability to separate data into different classes so that an accurate model can be built for fault detection, recognition and prediction. The propos...

In this chapter we give an introduction to ROC (‘receiver operating characteristics’) analysis and its applications to data mining. We argue that ROC analysis provides decision support for data mining in several ways. For model selection, ROC analysis establishes a method to determine the optimal model once the operating characteristics for the model deployment context are known. We also show h...

Data mining is concerned with developing algorithms and computational tools and techniques to help people extract patterns from data. In this paper an efficient data mining approach, which is based on fuzzy set theory and clonal selection algorithm, is proposed. The main motivation is to benefit from the global search performed by this kind of algorithms. Experimental results show the number of...

Determining and instantiating an appropriate data mining task and method is often rather difficult, especially for inexperienced users. In this paper, we present a methodological approach for capturing, reusing, and generalizing experiences (task-configurations) for an easier selection and instantiation of appropriate methods and tasks in descriptive data mining. We show how the cases describin...

OVA scheme vs. single machine approach in feature selection for microarray datasets p. 10 Similarity searching in DNA sequences by spectral distortion measures p. 24 Multispecies gene entropy estimation, a data mining approach p. 38 A unified approach for discovery of interesting association rules in medical databases p. 53 Named relationship mining from medical literature p. 64 Experimental st...

Heart disease is the leading cause of death in the world over the past 10 years. Researchers have been using several data mining techniques to help health care professionals in the diagnosis of heart disease patients. Decision Tree is one of the data mining techniques used in the diagnosis of heart disease showing considerable success. K-means clustering is one of the most popular clustering te...

With the exponential growth of the number of documents available on the Internet, automatic feature selection approaches are increasingly important for the preprocessing of textual documents for data mining. Feature selection, which focuses on identifying relevant data, can help reduce the workload of processing huge amounts of data as well as increase the accuracy for the subsequent data minin...