Unsupervised and semi-supervised machine learning is very advantageous in data-intensive applications. Density-based hierarchical clustering obtains a detailed description of the structures of clusters and outliers in a dataset through density ...${}^{\mathrm{}}$

Autoimmune diseases are the third highest cause of mortality in the world, and the identification of an anti-nuclear antibody via an immunofluorescence test for HEp-2 cells is a standard procedure to support diagnosis. In this work, we ...

• To compare different CNNs to classify HEp-2 cells in immunofluorescence images.

A key challenge for document clustering consists in finding a proper similarity measure for text documents that enables the generation of cohesive groups. Measures based on the classic bag-of-words model take into account solely the presence (and ...

Approaches that combine streaming algorithms and distributed computing have potential to deal with voluminous and high-speed data streams. Considering the data stream clustering task, also an important issue needs to be addressed, estimate the number of ...

The recent growing size of datasets requires scalability of data mining algorithms, such as clustering algorithms. The MapReduce programing model provides the scalability needed, alongside with portability as well as automatic data safety and ...

Dealing with distributed data is one of the challenges for clustering, as most clustering techniques require the data to be centralized. One of them, k-means, has been elected as one of the most influential data mining algorithms for being simple, ...

Dealing with big amounts of data is one of the challenges for clustering, which causes the need for distribution and management of huge data sets in separate repositories. New distributed systems have been designed to scale up from a single server to ...

One of the challenges for clustering resides in dealing with data distributed in separated repositories, because most clustering techniques require the data to be centralized. One of them, k-means, has been elected as one of the most influential ...

Dealing with big amounts of data is one of the challenges for clustering, which causes the need for distribution of large data sets in separate repositories. However, most clustering techniques require the data to be centralized. One of them, the k-...

Cluster ensemble aims at producing high quality data partitions by combining a set of different partitions produced from the same data. Diversity and quality are claimed to be critical for the selection of the partitions to be combined. To enhance these ...

Abstract: One of the top ten most influential data mining algorithms, k-means, is known for being simple and scalable. However, it is sensitive to initialization of prototypes and requires that the number of clusters be specified in advance. This paper ...

One of the most influential algorithms in data mining, k-means, is broadly used in practical tasks for its simplicity, computational efficiency and effectiveness in high dimensional problems. However, k-means has two major drawbacks, which are the need ...