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Data exploration is an approach similar to initial data analysis, whereby a data analyst uses visual exploration to understand what is in a dataset and the characteristics of the data, rather than through traditional data management systems. [1]
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]
In databases, brushing and linking is the connection of two or more views of the same data, such that a change to the representation in one view affects the representation in the other. [1] Brushing and linking is also an important technique in interactive visual analysis , a method for performing visual exploration and analysis of large ...
Analysts may apply a variety of techniques, referred to as exploratory data analysis, to begin understanding the messages contained within the obtained data. [30] The process of data exploration may result in additional data cleaning or additional requests for data; thus, the initialization of the iterative phases mentioned in the lead ...
Exploratory research is "the preliminary research to clarify the exact nature of the problem to be solved." It is used to ensure additional research is taken into consideration during an experiment as well as determining research priorities, collecting data and honing in on certain subjects which may be difficult to take note of without exploratory research.
OLAP clients include many spreadsheet programs like Excel, web application, SQL, dashboard tools, etc. Many clients support interactive data exploration where users select dimensions and measures of interest. Some dimensions are used as filters (for slicing and dicing the data) while others are selected as the axes of a pivot table or pivot chart.
Data visualization refers to the techniques used to communicate data or information by encoding it as visual objects (e.g., points, lines, or bars) contained in graphics. The goal is to communicate information clearly and efficiently to users. It is one of the steps in data analysis or data science. According to Vitaly Friedman (2008) the "main ...
On a data set consisting of mixtures of Gaussians, these algorithms are nearly always outperformed by methods such as EM clustering that are able to precisely model this kind of data. Mean-shift is a clustering approach where each object is moved to the densest area in its vicinity, based on kernel density estimation .