Data dimensionality is growing rapidly, which poses challenges to the vast majority of existing mining and learning algorithms, such as the curse of dimensionality, large storage requirement, and high computational cost. Feature selection has been proven to be an effective and efficient way to prepare high-dimensional data for data mining and machine learning. The recent emergence of novel tech...

Real life data sets often contain noisy data which makes the subsequent data mining process difficult. The feature selection preprocessing step can be simplified the datasets by eliminating the features that are redundant for classification process, with pertinent features would reduce the size of dataset and afterwards allow more apparent analysis of extracted rules pattern and rules. This pap...

Feature selection process is used to reduce the feature vector length and identify thediscriminative features. Many acoustic-phonetic features including Mel-Frequency CepstralCoefficient (MFCC), Energy, Pitch, Zero-crossing, spectrum were tested individually for Arabicmispronunciation detection using three classifiers; Random Forest, Bayesian classifier, BaggedSupport Vector Machine (SVM). The ...

Many data analysts require tools which can integrate their database management packages (e.g. Microsoft Access) with their data analysis ones (e.g. SAS, SPSS), and provide guidance for the selection of appropriate mining algorithms. In addition, the analysts need to extract and validate statistical results to facilitate data mining. In this paper, we describe an integrated data mining system ca...

Mining method selection is one of the most critical and problematic activities of mining engineering. The ultimate goals of mining method selection are to maximize company profit, maximize recovery of the mineral resources and provide a safe environment for the miners by selecting the method with the least problems among the feasible alternatives. Selection of an appropriate mining method is a ...

Data mining is a form of knowledge discovery required for solving problems in a specific domain. Classification is a technique used for discovering class labels of unknown data. Different methods for classification exists like bayesian, decision trees, rule based, neural networks etc. Before applying any mining technique, irrelevant and redundant features needs to be removed. Filtering is done ...

Nowadays there exist a number of data-mining techniques to extract knowledge from large databases. Recent research has shown that no single technique can dominate some other technique on all possible data-mining problems. Nevertheless, many empirical studies report that a technique or a group of techniques can perform significantly better than any other technique on a certain data-mining proble...

Data warehouse (DW) is a repository with query interface in support of Decision support systems. DW required answering many complex queries, managerial level queries and analytical queries, needing to develop advanced computing techniques. The DW system process involving data modeling, ETL process, query interface and reporting system. Materialized views (MV) are the pre calculated views which ...

Data warehouse (DW) is a repository with query interface in support of Decision support systems. DW required answering many complex queries, managerial level queries and analytical queries, needing to develop advanced computing techniques. The DW system process involving data modeling, ETL process, query interface and reporting system. Materialized views (MV) are the pre calculated views which ...

One of the most difficult tasks in the whole KDD process is to choose the right data mining technique, as the commercial software tools provide more and more possibilities together and the decision requires more and more expertise on the methodological point of view. Indeed, there are a lot of data mining techniques available for an environmental scientist wishing to discover some model from he...