"Data Mining refers to mining or mining" large amounts of knowledge. "Data Mining, should properly have been referred to the knowledge of data mining. There are many other terms that a similar or slightly different meanings in data mining, such as knowledge mining from databases, knowledge extraction, data / model analysis of archaeological data, and data dredging.
Because of their predictive power, data mining techniques are widely used for diagnostic and health care applications. Data mining algorithms can learn from past examples of the clinical data and model are often non-linear relationship between independent and dependent variables. The resulting model represents the formalized knowledge, which is often a good diagnostic advice.
The format is the most widely used technique for medical data mining. The format is widely used to analyze the extraction of biomedical databases.
"The current research in the field of mining are resolved Classification Study of biological databases, analysis and demonstration of a graph representation, using a naive Bayesian classification modeling of hidden patterns and techniques Hypothesis."
Steps in making the classification mining
1. Preprocessing & Transformation biological data: mining large biological databases, it is necessary to pre-process the data and biological information stored in databases, suitable for further processing in a simple text file.
1. Development of mathematical Modal Data: After the results of the pre processor naive Bayesian classification. Bayesian classifier is also exhibited high accuracy and speed when applied to large databases. The technique is the use of high categorical and numerical results. We want this method and other classification algorithm to analyze to find better results to biological data.
2. Implementation and validation: The user can implement the idea of using Oracle 10g package, or ASP dot net 2005, if we do not use a database of data pre processing, we validated the results with a K-fold cross validation to the optimal results found.
The reasons for the classification Basian extraction of biomedical informatics
In the field of biomedical informatics is a growing popularity and the attention and has grown rapidly in the past two decades. The progress of the new molecular, genomic and biomedical technologies and applications such as genome sequencing, protein identification, imaging and medical records of patients, biomedical research, huge amounts of data every day. Originating from the individual research and clinical practice in the biomedical information is also available in hundreds of public and private databases, which are made possible by the new database technologies and the Internet.
1. Biomedical researchers and practitioners are now faced with "info-glut" problem. Currently, the rate of data accumulation is much faster than the speed of data interpretation. This information must be effectively organized and analyzed in order to be useful.
2. New computational techniques and technologies are needed to manage these large data sets of biomedical data and useful patterns and knowledge to find them. In particular, knowledge management data mining and text mining methods successfully adopted in various biomedical applications in recent years.
3. Data pre processing is another major problem. We must develop effective means of treating the problem.
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