Open-source data mining tool orange

Orange is a component-based machine learning library that can be used for data mining through visual programming or Python scripts. It is applicable to beginners and experts, it can also be applied to bioinformatics and text mining through extension. Orange is a university in ruerya, Slovenia.
Of Ljubljana) is an open-source software developed and produced by the AI lab of the Institute of Computer and Information Science. Official Website: http://orange.biolab.si/screenshots/
Orange features: 1. Support for Visual Programming
2. Support for different visual images
3. Interactive Data Analysis
4. Big toolbox: more than 100 parts are being added continuously to cover standard data analysis tasks.
5. Support for python Interfaces
6. Support for Windows, Linux, Mac OS X, etc.
No visual programming materials are found on the Internet and in the installation package. Therefore, you can only learn through the python interface. Before using orange for data mining, you must convert the data to the corresponding format:
1. Text File suffixed with. Tab
2. Attribute names of the first behavior. Each attribute name is separated by a tab key.
3. Second behavior attribute characteristics: discrete or continuous, expressed in discrete or continuous, which can also be abbreviated as D or C
4. Optional additional information for the third action. For example, ignore this column using ignore (or I ).
5. Data is followed, and each column is separated by a tab.

Orange outlier Mining
Orngoutlier is a class provided by orange for outlier mining.

Algorithm: calculate the average distance from each sample to other samples, and then calculate the Z-score of all the average distances. The higher the Z-score, the longer the distance from the sample to other samples than the average distance.

You can set the number of distances (such as Euclidean distance or Euclidean distance) and neighbors. The Manhattan distance is used by default, and the number of neighbors is 0 by default.

Example:

      import orange, orngOutlierdata = orange.ExampleTable("car")outlierDet = orngOutlier.OutlierDetection()outlierDet.setExamples(data, orange.ExamplesDistanceConstructor_Euclidean(data))zValues = outlierDet.zValues()sorted = zValues[:]sorted.sort()for i, el in enumerate(zValues):if el >= sorted[-10]: print  i,"\t:",data[i],"Z-score: %5.3f" % el

This example uses car. the tab file is used as the data input, and the Euclidean distance is used to calculate the sample distance. The top 10 samples with the highest Z-score and Their Z-score are output. however, the calculation results seem to be not very good...