Data Mining Techniques - The Diigo Meta page

• 25 Sep 09
  

  StefanoStefano

  _Scienze & Manuali AI Data Mining

• 19 Sep 09
  

  John Rodrigues

  "Data Mining is an analytic process designed to explore data (usually large amounts of data - typically business or market related) in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by ap

  datamining analytics visualization research reference statistics database search ai

• 04 Sep 09
  

  Monika Sharma

  Data Preparation (in Data Mining)
  Data preparation and cleaning is an often neglected but extremely important step in the data mining process. The old saying "garbage-in-garbage-out" is particularly applicable to the typical data mining projects where large data sets collected via some automatic methods (e.g., via the Web) serve as the input into the analyses. Often, the method by which the data where gathered was not tightly controlled, and so the data may contain out-of-range values (e.g., Income: -100), impossible data combinations (e.g., Gender: Male, Pregnant: Yes), and the like. Analyzing data that has not been carefully screened for such problems can produce highly misleading results, in particular in predictive data mining.
  
  Data Reduction (for Data Mining)
  The term Data Reduction in the context of data mining is usually applied to projects where the goal is to aggregate or amalgamate the information contained in large datasets into manageable (smaller) information nuggets. Data reduction methods can include simple tabulation, aggregation (computing descriptive statistics) or more sophisticated techniques like clustering, principal components analysis, etc.
  
  See also predictive data mining, drill-down analysis.
  
  Deployment
  The concept of deployment in predictive data mining refers to the application of a model for prediction or classification to new data. After a satisfactory model or set of models has been identified (trained) for a particular application, one usually wants to deploy those models so that predictions or predicted classifications can quickly be obtained for new data. For example, a credit card company may want to deploy a trained model or set of models (e.g., neural networks, meta-learner) to quickly identify transactions which have a high probability of being fraudulent.
  
  Drill-Down Analysis
  The concept of drill-down analysis applies to the area of data mining, to denote the interactive exploration of data, in particular of large databases. The process of drill-down analyses begins by con

  Data Mining

• 15 Jul 09
  

  Régis Gaidot

  search visualization data research graph analysis analytics reference database statistics datamining ai stats clustering mining algorithms analyse behavior methodology

• 18 Feb 09
  

  Alex Yakyma

  data_mining techniques

• 20 Oct 08
  

  José Ramón Balsas

  datamining references article

• 31 Jan 08
  

  Varna Sri Raman

  Statistics

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  Avinash K

  statistics research

• 03 Nov 07
  

  Fiona Macintosh

  data_mining

• 02 Oct 07
  

  Thomas Knight

  database reference data mining analysis

• 07 Jun 07
  

  Mark Burris

  statistics datamining

• 06 May 07
  

  spag85

  datamining

• 06 Apr 07
  

  smorea

  data_collection_methods

• 01 Apr 07
  

  Susan Shapiro

  Informatics Statistics datamining

• 16 Feb 07
  

  Kasia Klimaszewska

  data_mining work

• 22 Jan 07
  

  Bill LaLonde

  datamining delicious

• 30 Dec 06
  

  Francisco Glez

  algorithms Reference datamining

• 12 Dec 06
  

  Zhi Min Choo

  datamining reference howto

• 25 Oct 06
  

  mat

  data data-mining database datamining imported:del.icio.us reference statistics stats

• 09 Jun 06
  

  Erica Bee

  DataMining reference

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