krst plot 2007
The KRST plot method is a statistical technique used to detect outliers or anomalies in a dataset based on the kurtosis and skewness of the data points. The plot helps identify data points that deviate significantly from the rest of the dataset.
Here are some software products that offer functionality related to outlier detection and anomaly detection in datasets using various statistical methods:
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RapidMiner
RapidMiner is a data science platform that offers advanced analytics capabilities, including outlier detection using various statistical techniques. mehr Info... -
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Tableau
Tableau is a data visualization tool that offers features for exploring data and identifying outliers through interactive visualizations. mehr Info... -
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KNIME
KNIME is an open-source data analytics platform that provides tools for outlier detection and anomaly detection in datasets through a visual programming interface. mehr Info...
If you are looking for alternative software products for outlier/anomaly detection, here are some other options to consider:
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Python (with libraries like NumPy & Pandas)
Python programming language along with libraries like NumPy and Pandas provides powerful tools for outlier detection and anomaly detection through statistical analysis. -
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Weka
Weka is a collection of machine learning algorithms for data mining tasks. It offers outlier detection capabilities through various statistical techniques. mehr Info... -
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Orange
Orange is an open-source data visualization and analysis tool that includes functionalities for outlier detection and anomaly detection in datasets. mehr Info...
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