Data Mining Practice

A Data Mining Practice is an Applied Practice that is focused on solving Real-World Data Mining Tasks.

• AKA: Data Mining, Data Mining Applied Practice.
• Context:
  • It can be performed by a Data Mining Practitioner.
  • It can overlap with:
    • a Machine Learning Practice/
    • a Statistics Practice.
  • It has a Data Mining Terminology that can be partially represented in a Data Mining Glossary.
• Counter-Example(s):
  • a Data Mining Academic Discipline
• See: Data Mining Subject Area.

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.
• http://searchsqlserver.techtarget.com/sDefinition/0,,sid87_gci211901,00.html
  • DEFINITION - Data mining is sorting through data to identify patterns and establish relationships.
  • Data mining parameters include:
    • Association - looking for patterns where one event is connected to another event
    • Sequence or path analysis - looking for patterns where one event leads to another later event
    • Classification - looking for new patterns (May result in a change in the way the data is organized but that's ok)
    • Clustering - finding and visually documenting groups of facts not previously known
    • Forecasting - discovering patterns in data that can lead to reasonable predictions about the future (This area of data mining is known as predictive analytics.)
• http://www.twocrows.com/glossary.htm
  • data mining: An information extraction activity whose goal is to discover hidden facts contained in databases. Using a combination of machine learning, statistical analysis, modeling techniques and database technology, data mining finds patterns and subtle relationships in data and infers rules that allow the prediction of future results. Typical applications include market segmentation, customer profiling, fraud detection, evaluation of retail promotions, and credit risk analysis.
• Quotes of usage
  • A working definition of data mining is the discovery of interesting, unexpected, or valuable structures in large datasets.
  • Data mining is the discovery of interesting, unexpected or valuable structures in large datasets.
  • We examine how data mining is used and outline some of its methods.
  • Data mining is defined as the identification of interesting structure in data.
  • On the data-mining front we have ...
  • The recession has boosted the importance of data mining as more businesses search for clues to increase revenues and decrease expenses.
  • How Europeans Are Using Data Mining
  • Using data mining to find out the most vulnerable
  • This area of data mining is known as predictive analytics.
  • ... the basis of data mining is to compress the given data by ...
  • The promise of data mining is compelling, and convinces many.
  • The goal of data mining is to extract ...
  • Much of data mining is about leveraging existing data to make useful predictions.
  • The third family line of data mining is machine learning, which ...

2000

• (Han & Kamber, 2000) => Jiawei Han, and Micheline Kamber. (2000). "Data Mining: Concepts and Techniques, 1st ed." Morgan Kaufmann. ISBN 1558604898 (alternate, search)

1999

• (Sukumar, 1999) => Rajagopal Sukumar. (1999). "Data Mining. Overview Presentation.

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.