Detection

Clustering

Type of unsupervised learning method. Generally, it is used as a process to find meaningful structure, explanatory underlying processes, generative features, and groupings inherent in a set of examples.

Methods

Density-Based Methods

These methods consider the clusters as the dense region having some similarities and differences from the lower dense region of the space. These methods have good accuracy and the ability to merge two clusters.

Hierarchical Based Methods

The clusters formed in this method form a tree-type structure based on the hierarchy. New clusters are formed using the previously formed one.

Partitioning Methods

These methods partition the objects into k partitions and each partition forms one cluster. This method is used to optimize an objective criterion similarity function such as when the distance is a major parameter.

Blind Source Separation

Blind Source Separation (BSS) refers to a problem where both the sources and the mixing methodology are unknown, only mixture signals are available for further separation process.

In several situations it is desirable to recover all individual sources from the mixed signal, or at least to segregate a particular source.

Principal component analysis (PCA)

Is a statistical procedure that allows you to summarize the information content in large data tables by means of a smaller set of “summary indices” that can be more easily visualized and analyzed.

Independent Component Analysis (ICA)

Is a powerful technique in the field of data analysis that allows you to separate and identify the underlying independent sources in a multivariate data set.

Non-negative matrix factorization (NNMF)

Is a group of algorithms in multivariate analysis and linear algebra where a matrix V is factorized into two matrices W and H, with the property that all three matrices have no negative elements.

This non-negativity makes the resulting matrices easier to inspect. Also, in applications such as the processing of audio spectrograms or muscular activity, non-negativity is inherent to the data being considered.