Data Mining Research Area
A data mining research area is a research area that focuses on data mining research topics.
- AKA: KDD Research Area.
- Context:
- It can be represented by a Data Mining Ontology.
- It can be associated with Data Mining Research Task.
- It can be closely associated to research questions in:
- Example(s):
- Counter-Example(s):
- See: KDD Domain, KDD Discipline, Research Field.
References
2007
- (Han et al., 2007) ⇒ Jiawei Han, Hong Cheng, Dong Xin, and Xifeng Yan. (2007). “Frequent Pattern Mining: current status and future directions.” In: Data Mining and Knowledge Discovery, 15(1). doi:10.1007/s10618-006-0059-1
- Frequent pattern mining has been a focused theme in data mining research for over a decade.
1996
- (Fayyad et al., 1996d) ⇒ Usama M. Fayyad, Gregory Piatetsky-Shapiro, and Padhraic Smyth. (1996). “From Data Mining to Knowledge Discovery in Databases.” In: AI Magazine, 17(3).
- Historically, the notion of finding useful patterns in data has been given a variety of names, including data mining, knowledge extraction, information discovery, information harvesting, data archaeology, and data pattern processing. The term data mining has mostly been used by statisticians, data analysts, and the management information systems (MIS) communities. It has also gained popularity in the database field. The phrase knowledge discovery in databases was coined at the first KDD workshop in 1989 (Piatetsky-Shapiro 1991) to emphasize that knowledge is the end product of a data-driven discovery. It has been popularized in the AI and machine-learning fields.
In our view, KDD refers to the overall process of discovering useful knowledge from data, and data mining refers to a particular step in this process. Data mining is the application of specific algorithms for extracting patterns from data. The distinction between the KDD process and the data-mining step (within the process) is a central point of this article. The additional steps in the KDD process, such as data preparation, data selection, data cleaning, incorporation of appropriate prior knowledge, and proper interpretation of the results of mining, are essential to ensure that useful knowledge is derived from the data. Blind application of data-mining methods (rightly criticized as data dredging in the statistical literature) can be a dangerous activity, easily leading to the discovery of meaningless and invalid patterns.
- Historically, the notion of finding useful patterns in data has been given a variety of names, including data mining, knowledge extraction, information discovery, information harvesting, data archaeology, and data pattern processing. The term data mining has mostly been used by statisticians, data analysts, and the management information systems (MIS) communities. It has also gained popularity in the database field. The phrase knowledge discovery in databases was coined at the first KDD workshop in 1989 (Piatetsky-Shapiro 1991) to emphasize that knowledge is the end product of a data-driven discovery. It has been popularized in the AI and machine-learning fields.
1995
- (Han et al., 1995) ⇒ Jiawei Han, Yongjian Fu, Krzysztof Koperski, Gabor Melli, Wei Wang, and Osmar R. Zaiane. (1995). “Knowledge Mining in Databases: An Integration of Machine Learning Methodologies with Database Technologies." Canadian AI Magazine, 38.
- QUOTE: Active research has been conducted on knowledge discovery in databases by the researchers in our group for years, with many interesting results published and a prototyped knowledge discovery system, DBMiner (previously called DBLearn), developed and demonstrated in several conferences.