Data, Text, and Web Mining Features and Functions
Data mining is sorting through data to identify patterns and establish relationships that can be used to predict future behavior. Text Mining is about looking for patterns in natural language text, and may be defined as the porcess of analyzing text and extract information from it for particular purpose. Web mining is the integration of information gathered by traditional data mining methodologies and techniques with information gathered over the World Wide Web.
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Data Mining
Data mining uses statistical analysis, artificial intelligence, and machine learning technologies to identify patterns that could not be found by manual analysis alone. Data mining covers the exploration and analysis of data, in great quantities, with the purpose of discovering patterns or rules, which are meaningful.
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Text Mining
Text mining is the computer-based discovery of previously unknown information by automatically extracting information from different sources of written text. A key element in text mining is the linking together of extracted information to form new facts or new hypotheses for further conventional means of exploration. The main difference between regular data mining and text mining is that text mining patterns are extracted from natural language text rather than from structured databases of facts.
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Web Mining
Web mining is the integration of information gathered by traditional data mining methodologies and techniques with information gathered over the World Wide Web.
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Algorithm and Statistical Tools
Tools and methods for algorithm and statistical data analysis. In mathematics and computer science an algorithm is a finite set of well-defined instructions for accomplishing some task which, given an initial state, will terminate in a corresponding recognizable end-state.
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Statistical Analytics
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Econometrics and Time Series
Econometrics apply statistical and mathematical methods to economic data to test and quantify economic theories and solutions to economic problems. Time series analysis accounts for the fact that data points taken over time may have an internal structure (such as autocorrelation, trend or seasonal variation) that should be accounted for.
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Analytics Features and Functions
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