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Data presentation architecture weds the science of numbers, data and statistics in discovering valuable information from data and making it usable, relevant and actionable with the arts of data visualization, communications, organizational psychology and change management in order to provide business intelligence solutions with the data scope ...
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.
Thus, the study of visualization of high-dimensional spaces is of central importance to TDA, although it does not necessarily involve the use of persistent homology. However, recent attempts have been made to use persistent homology in data visualization. [28] Carlsson et al. have proposed a general method called MAPPER. [29]
After the pre-processing step either automated analysis methods H S = {f s1, ..., f sq} (i.e., statistics, data mining, etc.) or visualization methods V S : S → V, V S = {f v1, ..., f vs} are applied to the data, in order to reveal patterns as shown in the figure above. [7] In general the following paradigm is used to process the data:
In natural language processing (NLP), a text graph is a graph representation of a text item (document, passage or sentence). It is typically created as a preprocessing step to support NLP tasks such as text condensation [ 1 ] term disambiguation [ 2 ] (topic-based) text summarization , [ 3 ] relation extraction [ 4 ] and textual entailment .
Text mining, text data mining (TDM) or text analytics is the process of deriving high-quality information from text. It involves "the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources."
Data visualization uses information displays (graphics such as, tables and charts) to help communicate key messages contained in the data. [46] Tables are a valuable tool by enabling the ability of a user to query and focus on specific numbers; while charts (e.g., bar charts or line charts), may help explain the quantitative messages contained ...
The purpose of scientific visualization is to graphically illustrate scientific data to enable scientists to understand, illustrate, and glean insight from their data. Research into how people read and misread various types of visualizations is helping to determine what types and features of visualizations are most understandable and effective ...
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