Spatial transcriptomics are a collection of genomic technologies that have enabled
transcriptomic profiling on tissues with spatial localization information. Analyzing spatial …

This textbook explores the different aspects of data mining from the fundamentals to the
complex data types and their applications, capturing the wide diversity of problem domains …

An integrated framework for density-based cluster analysis, outlier detection, and data
visualization is introduced in this article. The main module consists of an algorithm to …

A new density-based clustering algorithm, RNN-DBSCAN, is presented which uses reverse
nearest neighbor counts as an estimate of observation density. Clustering is performed …

We explore the idea of evidence accumulation (EAC) for combining the results of multiple
clusterings. First, a clustering ensemble-a set of object partitions, is produced. Given a data …

Anomaly detection problems arise in multiple applications, as discussed in the preceding
chapter. such as financial fraud, cyber intrusion, video surveillance, and medical image …

Finding clusters in data, especially high dimensional data, is challenging when the clusters
are of widely differing shapes, sizes, and densities, and when the data contains noise and …

Background The use of clustering methods for the discovery of cancer subtypes has drawn a
great deal of attention in the scientific community. While bioinformaticians have proposed …

The explosive growth of malware variants poses a major threat to information security.
Traditional anti-virus systems based on signatures fail to classify unknown malware into their …

We address the problem of robust clustering by combining data partitions (forming a
clustering ensemble) produced by multiple clusterings. We formulate robust clustering under …