Unsupervised Machine Learning Methods

Unsupervised learning extracts features from data without any human provided classification hints. Most methods involve cluster analysis and grouping based on Euclidean or probabilistic distance. Techniques include hierarchical clustering, k-Means clustering, Gaussian and self-organising maps. Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) and combinations thereof are particularly useful for detecting historical patterns in data.

The large choice of techniques and configurable parameters, called hyperparameters, for each of the techniques means without some experience it takes a long time to stumble upon the optimum ways of working with time series data. Given that the learning parts of machine learning can, in some instances, take days or months just to try out one method with one set of hyperparameters, starting from scratch usually isn’t viable. The number of research permutations is too large.

To complicate matters, techniques also have to be assessed not just on accuracy but also on how easily they can be implemented in real rather than ‘desk’ situation. We cover this more this in Focus on Practical Matters.

SensorCognition™ bypasses these problems as it encapsulates our knowledge of the set of techniques most suitable for use with time series data. Our Edge device provides a ready made system for data capture suitable both for prototyping and actual real use.