Clustering algorithms have become one of the most critical research areas in multiple
domains, especially data mining. However, with the massive growth of big data applications …

Technological advancement has enabled us to store and process huge amount of data in
relatively short spans of time. The nature of data is rapidly changing, particularly its …

Information theoretic based measures form a fundamental class of similarity measures for
comparing clusterings, beside the class of pair-counting based and set-matching based …

In this work, we propose a new unsupervised image segmentation approach based on
mutual information maximization between different constructed views of the inputs. Taking …

Electroencephalography (EEG) signals can reflect activities of the human brain and
represent different emotional states. However, recognizing emotions based on full-channel …

One of the main obstacles in using exact optimisation methods for open pit production
scheduling is the size of real mining problems, which forms an intractable optimisation …

Categorical data are widely available in many real-world applications, and to discover
valuable patterns in such data by clustering is of great importance. However, the lack of a …

Similarity or distance between two objects plays a fundamental role in many data mining
tasks like classification and clustering. Categorical data, unlike numeric data, conceptually is …

Stroke survivors often suffer from movement impairments that significantly affect their daily
activities. The advancements in sensor technology and IoT have provided opportunities to …

Density Peaks Clustering (DPC) is a density-based clustering algorithm that has the
advantage of not requiring clustering parameters and detecting non-spherical clusters. The …