We discuss the challenges of the 2009 KDD Cup along with our ideas and methodologies for modelling the problem. The main stages included aggressive nonparametric feature selection, careful treatment of categorical variables and tuning a gradient boosting machine under Bernoulli loss with trees.

The paper describes several data mining techniques, developed to solve problems which are faced by biologists in Bioinformatics.Several biclustering algorithms which perform clustering on the two dimensions simultaneously are described. Other techniques described in this paper include feature selection methods which help in reducing noise and improving the performance of the classification model.

Most Data Mining tasks are performed by the application of Machine Learning techniques. Metaheuristic approaches are becoming very useful for designing efficient tools in Machine Learning. Metaheuristics are general strategies to design efficient heuristic procedures. Scatter Search is a recent metaheuristic that has been successfully applied to solve standard problems in three central paradigm...

In this paper, we present a novel approach to detect unknown virus using dynamic instruction sequences mining techniques. We collect runtime instruction sequences from unknown executables and organize instruction sequences into basic blocks. We extract instruction sequence patterns based on three types of instruction associations within derived basic blocks. Following a data mining process, we ...

------------------------------------------------------------------ABSTRACT------------------------------------------------------------------A credit-risk evaluation decision involves processing huge volumes of raw data, and hence requires powerful data mining tools. Several techniques that were developed in machine learning have been used for financial credit-risk evaluation decisions. Data min...

The Data Mining OPtimization Ontology (DMOP) has been developed to support informed decision-making at various choice points of the data mining process. The ontology can be used by data miners and deployed in ontology-driven information systems. The primary purpose for which DMOP has been developed is the automation of algorithm and model selection through semantic meta-mining that makes use of...

Real-world data mining applications often confront us with complex and noisy data, which makes it necessary to optimize the data mining models thoroughly to achieve high-quality results. We describe in this contribution an approach to tune the parameters of the model and the feature selection conjointly. The aim is to use one framework to solve a variety of tasks. We show that tuning is of larg...

In this research, a novel approach is developed to predict stocks return and risks. In this three stage method, through a comprehensive investigation all possible features which can be effective on stocks risk and return are identified. Then, in the next stage risk and return are predicted by applying data mining techniques for the given features. Finally, we develop a hybrid algorithm, on the ...