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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 ...
The term p-hacking (in reference to p-values) was coined in a 2014 paper by the three researchers behind the blog Data Colada, which has been focusing on uncovering such problems in social sciences research. [3] [4] [5] Data dredging is an example of disregarding the multiple comparisons problem. One form is when subgroups are compared without ...
Dimensionally cursed phenomena occur in domains such as numerical analysis, sampling, combinatorics, machine learning, data mining and databases. The common theme of these problems is that when the dimensionality increases, the volume of the space increases so fast that the available data become sparse. In order to obtain a reliable result, the ...
Clustering high-dimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions.Such high-dimensional spaces of data are often encountered in areas such as medicine, where DNA microarray technology can produce many measurements at once, and the clustering of text documents, where, if a word-frequency vector is used, the number of dimensions ...
Semantic data mining is a subset of data mining that specifically seeks to incorporate domain knowledge, such as formal semantics, into the data mining process.Domain knowledge is the knowledge of the environment the data was processed in. Domain knowledge can have a positive influence on many aspects of data mining, such as filtering out redundant or inconsistent data during the preprocessing ...
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]
An Inference Attack is a data mining technique performed by analyzing data in order to illegitimately gain knowledge about a subject or database. [1] A subject's sensitive information can be considered as leaked if an adversary can infer its real value with a high confidence. [2] This is an example of breached information security.
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.