Prognostics and health management (PHM) is a framework that offers comprehensive yet
individualized solutions for managing system health. In recent years, PHM has emerged as …

In this paper, we study what are the most important factors that deteriorate the performance
of the k-means algorithm, and how much this deterioration can be overcome either by using …

Data preprocessing is a major and essential stage whose main goal is to obtain final data
sets that can be considered correct and useful for further data mining algorithms. This paper …

Performance of iterative clustering algorithms which converges to numerous local minima
depend highly on initial cluster centers. Generally initial cluster centers are selected …

This paper presents a fault diagnosis technique based on acoustic emission (AE) analysis
with the Hilbert–Huang Transform (HHT) and data mining tool. HHT analyzes the AE signal …

Spectral clustering refers to a flexible class of clustering procedures that can produce high-
quality clusterings on small data sets but which has limited applicability to large-scale …

A scheme for the automatic detection of nodules in thoracic computed tomography scans is
presented and extensively evaluated. The algorithm uses the local image features of shape …

We present a method for initialising the K-means clustering algorithm. Our method hinges on
the use of a kd-tree to perform a density estimation of the data at various locations. We then …

This paper describes an automatic tissue segmentation method for newborn brains from
magnetic resonance images (MRI). The analysis and study of newborn brain MRI is of great …

Conventional methods of lung segmentation rely on a large gray value contrast between
lung fields and surrounding tissues. These methods fail on scans with lungs that contain …