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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 ...
List of GitHub repositories of the project: Kubernetes SIGs This data is not pre-processed List of GitHub repositories of the project: Konveyor This data is not pre-processed List of GitHub repositories of the project: RedHat Marketplace This data is not pre-processed List of GitHub repositories of the project: Redhat blog This data is not pre ...
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]
Exploratory data analysis is an analysis technique to analyze and investigate the data set and summarize the main characteristics of the dataset. Main advantage of EDA is providing the data visualization of data after conducting the analysis.
Data analysis, machine learning: Research in the various fields of data analysis and machine learning, such as prediction of market value of companies'shares and analysis of medical data [181] Yes DistrRTgen 2008-01-12 [182] 2014-06 [183] Independent [14] Cryptography: Analysis of hash strength for password security by developing rainbow tables ...
Polls, ratings and analysis of the 2012 Senate races 8/12 Convention Mention Meter Crowd-sourced quantification of rhetoric at the Democratic and GOP conventions
gretl is an example of an open-source statistical package. ADaMSoft – a generalized statistical software with data mining algorithms and methods for data management; ADMB – a software suite for non-linear statistical modeling based on C++ which uses automatic differentiation; Chronux – for neurobiological time series data; DAP – free ...
The main steps in forensic analytics are data collection, data preparation, data analysis, and reporting. For example, forensic analytics may be used to review an employee's purchasing card activity to assess whether any of the purchases were diverted or divertible for personal use.
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