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Several algorithms exist to help with analysis of data – Dijkstra's algorithm, breadth-first search, and depth-first search. Link analysis focuses on analysis of relationships among nodes through visualization methods (network charts, association matrix). Here is an example of the relationships that may be mapped for crime investigations: [3]
A variety of data re-sampling techniques are implemented in the imbalanced-learn package [1] compatible with the scikit-learn Python library. The re-sampling techniques are implemented in four different categories: undersampling the majority class, oversampling the minority class, combining over and under sampling, and ensembling sampling.
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]
Tukey defined data analysis in 1961 as: "Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data." [3]
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 profiling utilizes methods of descriptive statistics such as minimum, maximum, mean, mode, percentile, standard deviation, frequency, variation, aggregates such as count and sum, and additional metadata information obtained during data profiling such as data type, length, discrete values, uniqueness, occurrence of null values, typical string patterns, and abstract type recognition.
Educational data mining Cluster analysis is for example used to identify groups of schools or students with similar properties. Typologies From poll data, projects such as those undertaken by the Pew Research Center use cluster analysis to discern typologies of opinions, habits, and demographics that may be useful in politics and marketing.
The study of multiway data analysis was first formalized as the result of a conference held in 1988. The result of this conference was the first text specifically addressed to this field, Coppi and Bolasco's Multiway Data Analysis. [1] At that time, the application areas for multiway analysis included statistics, econometrics and psychometrics.
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