Search results for "K-means clustering"
showing 8 items of 18 documents
Automatic Unsupervised Segmentation of Retinal Vessels Using Self-Organizing Maps and K-Means Clustering
2011
In this paper an automatic unsupervised method for the segmentation of retinal vessels is proposed. A Self-Organizing Map is trained on a portion of the same image that is tested and K-means clustering algorithm is used to divide the map units in 2 classes. The entire image is again input for the Self-Organizing Map, and the class of each pixel will be the class of the best matching unit on the Self-Organizing Map. Finally, the vessel network is post-processed using a hill climbing strategy on the connected components of the segmented image. The experimental evaluation on the publicly available DRIVE database shows accurate extraction of vessels network and a good agreement between our segm…
The Hydrothermal System of Solfatara Crater (Campi Flegrei, Italy) Inferred From Machine Learning Algorithms
2019
Two machine learning algorithms were applied to three multivariate datasets acquired at Solfatara volcano. Our aim was to find an unbiased and coherent synthesis among the large amount of data acquired within the crater and along two orthogonal vertical NNE- and WNW-trending cross-sections. The first algorithm includes a new approach for a soft K-means clustering based on the use of the silhouette index to control the color palette of the clusters. The second algorithm which uses the self-organizing maps incorporates an alternative method for choosing the number of nodes of the neural network which aims to avoid the need for downstream clustering of the results of the classification. Both m…
Automatic detection of cervical cells in Pap-smear images using polar transform and k-means segmentation
2016
We introduce a novel method of cell detection and segmentation based on a polar transformation. The method assumes that the seed point of each candidate is placed inside the nucleus. The polar representation, built around the seed, is segmented using k-means clustering into one candidate-nucleus cluster, one candidate-cytoplasm cluster and up to three miscellaneous clusters, representing background or surrounding objects that are not part of the candidate cell. For assessing the natural number of clusters, the silhouette method is used. In the segmented polar representation, a number of parameters can be conveniently observed and evaluated as fuzzy memberships to the non-cell class, out of …
Beyond Tandem Analysis: Joint Dimension Reduction and Clustering in R
2019
We present the R package clustrd which implements a class of methods that combine dimension reduction and clustering of continuous or categorical data. In particular, for continuous data, the package contains implementations of factorial K-means and reduced K-means; both methods combine principal component analysis with K-means clustering. For categorical data, the package provides MCA K-means, i-FCB and cluster correspondence analysis, which combine multiple correspondence analysis with K-means. Two examples on real data sets are provided to illustrate the usage of the main functions.
Pattern Classification from Multi-beam Acoustic Data Acquired in Kongsfjorden
2021
Climate change is causing a structural change in Arctic ecosystems, decreasing the effectiveness that the polar regions have in cooling water masses, with inevitable repercussions on the climate and with an impact on marine biodiversity. The Svalbard islands under study are an area greatly influenced by Atlantic waters. This area is undergoing changes that are modifying the composition and distribution of the species present. The aim of this work is to provide a method for the classification of acoustic patterns acquired in the Kongsfjorden, Svalbard, Arctic Circle using multibeam technology. Therefore the general objective is the implementation of a methodology useful for identifying the a…
Radio frequency fingerprinting for outdoor user equipment localization
2017
The recent advancements in cellular mobile technology and smart phone usage have opened opportunities for researchers and commercial companies to develop ubiquitous low cost localization systems. Radio frequency (RF) fingerprinting is a popular positioning technique which uses radio signal strength (RSS) values from already existing infrastructures to provide satisfactory user positioning accuracy in indoor and densely built outdoor urban areas where Global Navigation Satellite System (GNSS) signal is poor and hard to reach. However a major requirement for the RF fingerprinting to maintain good localization accuracy is the collection and updating of large training database. The Minimization…
CLUSTERING INCOMPLETE SPECTRAL DATA WITH ROBUST METHODS
2018
Abstract. Missing value imputation is a common approach for preprocessing incomplete data sets. In case of data clustering, imputation methods may cause unexpected bias because they may change the underlying structure of the data. In order to avoid prior imputation of missing values the computational operations must be projected on the available data values. In this paper, we apply a robust nan-K-spatmed algorithm to the clustering problem on hyperspectral image data. Robust statistics, such as multivariate medians, are more insensitive to outliers than classical statistics relying on the Gaussian assumptions. They are, however, computationally more intractable due to the lack of closed-for…
An Examination of Tourist Arrivals Dynamics Using Short-Term Time Series Data: A Space—Time Cluster Approach
2013
The purpose of this study is to examine the development of Italian tourist areas ( circoscrizioni turistiche) through a cluster analysis of short time series. The technique is an adaptation of the functional data analysis approach developed by Abraham et al (2003), which combines spline interpolation with k-means clustering. The findings indicate the presence of two patterns (increasing and stable) averagely characterizing groups of territories. Moreover, tests of spatial contiguity suggest the presence of ‘space–time clusters’; that is, areas in the same ‘time cluster’ are also spatially contiguous. These findings appear to be more robust in particular for those series characterized by an…