Data mining methods

Handling of complex data sets Visual data mining methods especially show huge advantages over classical approaches if only Httle information about the data is known or if the expected patterns and relationships are not clearly defined. Furthermore, very inhomogeneous data sets or data with a high noise level can still be analyzed by these methods. 

The described computational tools provide interactive, fast, and flexible data visualizations of chemical data that help and even enhance the human thought processes. However, visualization alone is often inadequate when multiple data points must be considered. A number of data mining methods that seek to identify significant relationships in large multidimensional databases are now being used for library design. 

Levine JG, Tonning JM, Szarfman A. Reply The evaluation of data mining methods for the simultaneous and systematic detection of safety signals in large databases lessons to be learned. Br J Clin Pharmacol. 2006 61 105-13. 

Geppert H, Vogt M, Bajorath J (2010) Current trends in ligand-based virtual screening molecular representations, data mining methods, new application areas, and performance evaluation. J Chem Inf Model 50(2) 205-216... 

Over the past 60 years a multitude of means have been used to capture SARs. We can broadly divide them into two groups those based on statistical or data mining methods (e.g., regression models) and those based on physical approaches (e.g., pharmacophore models). For a comprehensive review of quantitative structure-activity relationship (QSAR) methodologies the reader is referred... 

Data mining methods can be generally divided into two types, unsupervised and supervised. Whereas unsupervised methods seek informative patterns, which directly display the interesting relationship among the data, supervised methods discoverpredictivepatterns, which can be used later to predict one or more attributes from the rest. 

A wide variety of supervised data mining methods have been applied for analyzing... 

We are interested in a data-mining or data reduction method that is as unsupervised as possible. In other words, we are interested in the data analysis method that is maximally data-driven. We do not mean that we insist that data-mining methods must be unsupervised, but we believe that supervising or guidance could do best with methods that can already allow sufficiently powerful data reduction in the unsupervised mode. 

B. What is the difference III. DATA MINING METHODS using parallel in silico... 

Kubat, M., I. Bratko, and R. S. Michalski. 1997. A review of machine learning methods. In Machine learning and data mining methods and applications, ed. M. Kubat, I. Bratko, and R. S. Michalski, 3. New York Wiley. 

Mining of the data. In this step, methods are applied for the enumeration of patterns or compilation of models. Many different data mining methods can be used to extract information from the preprocessed target database. The most suitable method is determined in the next step of the process. 

A general concern in data mining is the representation of objects. Molecules, text documents, images, nucleic acid, or protein sequences all represent nonnumerical objects. However, all data mining methods require the transformation of objects into an algebraic, i.e., numerical, representation. 

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