Data Mining is a set of interdisciplinary procedures for discovering beforehand undisclosed, significant, practically helpful, and accessible data patterns indispensable for decision making in different areas of human activity. A term invented by Gregory Pyatetsky-Shapiro in 1989.
The basis of the Data Mining consists of various methods of classification, modeling, and forecasting, which are based on using decision trees, neural networks, genetic algorithms, evolutionary programming, associative memory, fuzzy logic, etc.
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Methods for Data Mining often include statistical methods:
- descriptive analysis,
- correlation and regression analysis,
- factor analysis,
- variance analysis,
- component analysis,
- discriminant analysis,
- time series analysis.
These methods, however, require some preliminary knowledge about the data to be analyzed, which somewhat contradicts the goals of Data Mining (the discovery of previously unknown non-trivial and practically useful knowledge).
One of the major purposes of the Data Mining is a visual representation of the results of calculations, which allows Data Mining tools be used by people without special mathematical training. At the same time, the application of the data analysis statistical methods requires a good knowledge of the probability theory and mathematical statistics.
Nowadays, high technologies are taking more and more important role in the process of taking the most important decisions. Information processing by super-powered cluster computers yield results comparable to usefulness of mining. The students, who write their research paper on data mining, have to by all means take into account the value of the knowledge discovery processes in the modern world. In their studies, students of universities and colleges are required to examine strictly the etymology of the term “data mining”, study the history of the emergence of this phenomenon and its evolution. It is very important to identify carefully all the methods that make up the process of data mining, to navigate freely in the wilds of this complicated issue. Your own ideas on how to further develop the technology, which in the future will be more and more widespread, will certainly be appreciated.
To write a good research paper on data mining as well as data warehousing, the investigators should focus on comparing the critical components that compile the totality of the knowledge discovering methods. You should be able to analyze all the nuances that can be recognized only by painstaking inspection. But it is not enough to know perfectly the subject. The results of your research should be present in such a manner so that not one small part of your immense research left undisclosed. To find out how to approach proper writing of a first-class research paper, you can familiarize yourself with sample research papers. These free examples will teach you how to write good research proposals on data mining techniques. You can find a lot of them on the Web, but be cautious to choose.
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