Search results
Results from the WOW.Com Content Network
An example of data mining related to an integrated-circuit (IC) production line is described in the paper "Mining IC Test Data to Optimize VLSI Testing." [12] In this paper, the application of data mining and decision analysis to the problem of die-level functional testing is described. Experiments mentioned demonstrate the ability to apply a ...
Data Mining and Knowledge Discovery is a bimonthly peer-reviewed scientific journal focusing on data mining published by Springer Science+Business Media.It was started in 1996 and launched in 1997 by Usama Fayyad as founding Editor-in-Chief by Kluwer Academic Publishers (later becoming Springer).
AMiner (ArnetMiner) is designed to search and perform data mining operations against academic publications on the Internet, using social network analysis to identify connections between researchers, conferences, and publications. [1]
The outer circle in the diagram symbolizes the cyclic nature of data mining itself. A data mining process continues after a solution has been deployed. The lessons learned during the process can trigger new, often more focused business questions, and subsequent data mining processes will benefit from the experiences of previous ones.
The objective of these models is to assess the possibility that a unit in another sample will display the same pattern. Predictive model solutions can be considered a type of data mining technology. The models can analyze both historical and current data and generate a model in order to predict potential future outcomes. [14]
The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large ...
Affinity analysis falls under the umbrella term of data mining which uncovers meaningful correlations between different entities according to their co-occurrence in a data set. In almost all systems and processes, the application of affinity analysis can extract significant knowledge about the unexpected trends [ citation needed ] .
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]