Dimensionality Reduction

Dimensionality reduction is an important task in machine learning which facilities analysis, compression and visualization of high dimensional data. The problem is that most of the times the machine learning algorithms (e.g. methos for clustering, classification, regression,...

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K-Means Algorithm

It is one of the simplest and most efficient clustering algorithms proposed in the literature of data clustering. It corresponds to a flat (or partitional) algorithm, which means that the dataset is classified  into multiple groups in one shot based on the characteristics...

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Distance-Based Algorithms for Clustering

The differences of clustering algorithms, as explained on the Data Clustering post, lies in how "similarity" is defined. In probabilistic models, the main idea is to model the data from a generative process, where a specific distribution is assumed (e.g. Gaussian), then parameters...

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