README.md

Python HINMine

HINMine algorithm for network-based propositionalization

Algorithm description

The HINMINE algorithm for network-based propositionalization is an algorithm for data analysis based on network analysis methods.

The input for the algorithm is a data set containing instances with real-valued features. The purpose of the algorithm is to construct a new set of features for further analysis by other data mining algorithms. The algorithm outputs a data set with features, generated for each data instance in the input data set. The features represent how close a given instance is to the other instances in the data set. The closeness of instances is measured using the PageRank algorithm, calculated on a network constructed from instance similarities.

The algorithm has two parameters:

• normalize: [default value True] This parameter is a boolean parameter which tells the algorithm whether it should first normalize the data. Data normalization normalizes the range of each feature to [-1, 1] and can be useful when comparing two instances. However, if two features have different values for a non-arbitrary reason, normalization should not be performed
• damping: [default value 0.85] This parameter is the damping factor of the PageRank algorithm used in calculating feature values for the data instances. It represents the probability of a random walker in a network to continue its random walk as oposed to teleporting to a random node

Build (for contributors)

Run: ./build.sh

Test (for contributors)

Run: ./tests/test.sh

Publish (for contributors)

Run: ./publish.sh