GM-RKB: Data Mining Task

A Data Mining Task is a Discovery Task that requires the discovery of Novel Patterns and Useful Patterns from Large Data Sets.

AKA: Data Mining, Database Mining Task.
Context:
- It can be the focus of a Data Mining Academic Discipline, that can be:
  - analyzed by Data Mining Research and reported at a Data Mining Conference or Data Mining Journal.
  - taught Data Mining Education.
  - applied by the Data Mining Practice.
- It can be a part of:
  - a Posthoc Analysis Task.
  - an Experimental Analysis Task.
- It can be:
  - an Exploratory Data Analysis Task, that formulates Hypotheses.
  - a Confirmatory Data Analysis Task, that tests Hypotheses.
  - a Predictive Modeling Task, that exploits Hypotheses.
- It can be:
  - a General Data Mining Task.
    - a Descriptive Data Mining Task/Exploratory Data Analysis Task, such as:
    - a Predictive Data Mining Task/Predictive Modeling Task/.
  - a Domain Specific Data Mining Task.
    - a Scientific Data Mining Task.
    - a Commercial Data Mining Task.
- It can be:
  - performed by a Data Miner.
  - proposed and researched by a Data Mining Researcher.
- It can be seend from:
Example(s):
- a Graph Mining Task
- a Text Mining Task
- a Web Mining Task
- an Item Recommendation Task, such as a Movie Recommendation Task.
- an Association Rule Task that describes which goods sell together so that a store manager can optimize their customer's shopping experience.
- an Information Extraction Task, which might require the identification of Semantic Relations from the web in order to put structure to the data. http://www.citeseer.com
Counter-Example(s):
- a Character Recognition Task.
- a Game Playing Learning Task.
- a Autonomous Agent Learning Task.
- a Parametric Modeling Task, because it cannot produce a Novel Pattern.
See: Inductive Reasoning, Knowledge Discovery.

References

2009

(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,[1] 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.
- While data mining can be used to uncover patterns in data samples, it is important to be aware that the use of non-representative samples of data may produce results that are not indicative of the domain. Similarly, data mining will not find patterns that may be present in the domain, if those patterns are not present in the sample being "mined". There is a tendency for insufficiently knowledgeable "consumers" of the results to attribute "magical abilities" to data mining, treating the technique as a sort of all-seeing crystal ball. Like any other tool, it only functions in conjunction with the appropriate raw material: in this case, indicative and representative data that the user must first collect. Further, the discovery of a particular pattern in a particular set of data does not necessarily mean that pattern is representative of the whole population from which that data was drawn. Hence, an important part of the process is the verification and validation of patterns on other samples of data.
- The term data mining has also been used in a related but negative sense, to mean the deliberate searching for apparent but not necessarily representative patterns in large amounts of data. To avoid confusion with the other sense, the terms data dredging and data snooping are often used. Note, however, that dredging and snooping can be (and sometimes are) used as exploratory tools when developing and clarifying hypotheses.

2006

(Han & Kamber, 2006) => Jiawei Han ...

2000

(Witten & Frank, 2000) => Ian H. Witten, and Eibe Frank. (2000). "Data Mining: practical machine learning tools and techniques with Java implementations. Morgan Kaufmann.
- The data used for mining has almost certainly not been gathered expressly for that purpose.

1998

(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.