A scientific experiment is repeatable. Pseudoscience makes claims that cannot be either confirmed or denied. Both seem to want to explain our experiences and broaden our understanding. Science, as a working method, employs basic principles such as objectivity and accuracy to establish a finding. It often also uses certain admitted assumptions about reality, assumptions that must eventually support themselves and be proven, or the resulting finding fails verification. Pseudoscience, however, uses invented modes of analysis which it pretends or professes meet the requirements of scientific method, but which in fact violate it's essential attributes. Many obvious examples of pseudoscience are easy to identify, but the more subtile and herefore more insidious and convincing cases.
It is a computer aided technique of searching and evaluating a bulk of data in order to obtain useful information.
This technique searches through the data to find hidden patterns and uses statistical methods to find relationship among data for finding predictive information and for classification of data.
This is a mixture of different disciplines which include machine learning, statistics and artificial intelligence and also some mathematical methods.
Often the useful information is extracted from enormous database using modeling technique which is used to build a model from instances of the data where the solution is known and later apply this model on the instances where the solution is unknown.
In unsupervised data mining, the data analysis is not done by using modeling technique. In other words the labels are provided in order not draw inferences and prediction from data sets. Example is clustering.
In supervised data mining the model is developed to make inferences and classification. Example is neural networks.
For example data mining is used in Medicine industry to provide more accurate diagnostics and treatments on the basis of patient's medical history , physical examination or different patient tests data.
Data mining also makes it possible to manage health resources more efficiently and cost-effectively by detecting risks, predicting diseases in certain sections of the population or predicting hospitalization duration.
