Unsupervised data mining

Unsupervised Data Mining. Searching large volumes of data for hidden descriptive relationships. Unlike supervised data mining, no response variables are used. The techniques used include various display and data reduction methods, as well as cluster analysis and association analysis. 

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. 

Pattern Recognition. The application of computers to build descriptive or predictive models (i.e., find patterns) of information from input datasets. The techniques of pattern recognition overlap those used in statistics, chemometrics, and data mining, and include data display, description, and reduction, unsupervised methods such as cluster analy-... 

In this chapter, we would like to demonstrate that one of the very old MVA tools, nonmetric multidimensional scaling (nMDS) [3], can work well as an unsupervised truly data-driven method for data reduction. We first explain an efficient maximally nonmetric algorithm [4] and then demonstrate its superiority to linear MVA methods. We also demonstrate that the subsequent application of linear MVA after data reduction by nMDS can often be a powerful data mining technique. 

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. 

Selected Data Mining Techniques for Gene Expression 9.15.3.1 Unsupervised Methods in Bioinformatics... 

The learning algorithm can be performed in supervised or unsupervised mode. In order to avoid bias, it is advisable to start data mining investigations of datasets with unsupervised learning methods. 

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