Data Science Discipline
- AKA: Data Mining Field.
- It must include:
- It can support a Data Mining Practice.
- It can advance a Data Mining Terminology.
- It can develop prototype data mining systems, such as SVMlight, MALLET, and Weka.
- It can be related to: a Statistics Discipline, a Machine Learning Discipline.
- See: Data Mining Algorithm, Data Mining System.
- (Wikipedia, 2009) ⇒ http://en.wikipedia.org/wiki/Data_mining
- Data mining is the process of extracting hidden patterns from data. As more data is gathered, with the amount of data doubling every three years, data mining is becoming an increasingly important tool to transform this data into information. It is commonly used in a wide range of profiling practices, such as marketing, surveillance, fraud detection and scientific discovery.
- (Hand et al., 2001) ⇒ David J. Hand, Heikki Mannila, and Padhraic Smyth. (2001). “Principles of Data Mining." MIT Press. ISBN:026208290X
- (Kohavi & Provost, 1998) ⇒ Ron Kohavi, and Foster Provost. (1998). “Glossary of Terms.” In: Machine Leanring 30(2-3).
- Data mining: The term data mining is somewhat overloaded. It sometimes refers to the whole process of knowledge discovery and sometimes to the specific machine learning phase.