Analyzing Microarray Gene Expression Data

TL;DR: This paper provides an overview of several commonly used approaches to clustering multiple longitudinal features, with an emphasis on application and implementation through R software and compares these approaches using real‐life and simulated datasets.

TL;DR: This thesis focuses on the area of clustering called model-based clustering, where it is assumed that data arise from a finite number of subpopulations, each of which follows a known statistical distribution and cross-validation is applied to select the number of groups and covariance structure within a family of Gaussian mixture models.

TL;DR: In this paper , a measurement system that uses a four microphones array and a data-driven algorithm to estimate depth of cut during end milling operations is presented, where the audible range acoustic emission signals captured with the microphones are combined using a spectral subtraction and a blind source separation algorithm to reduce the impact of noise and reverberation.

TL;DR: This work presents a new technique that improves the accuracy of record-and-replay approaches, without requiring prior knowledge of the services, and introduces a modified NeedlemanWunsch algorithm for distance calculation during message matching, wildcards in message prototypes for high variability sections, and entropy-based weightings in distance calculations for increased accuracy.