Search results for "cluster analysis."
showing 10 items of 805 documents
Automatic Detection of Hemangioma through a Cascade of Self-organizing Map Clustering and Morphological Operators
2016
Abstract In this paper we propose a method for the automatic detection of hemangioma regions, consisting of a cascade of algorithms: a Self Organizing Map (SOM) for clustering the image pixels in 25 classes (using a 5x5 output layer) followed by a morphological method of reducing the number of classes (MMRNC) to only two classes: hemangioma and non-hemangioma. We named this method SOM-MMRNC. To evaluate the performance of the proposed method we have used Fuzzy C-means (FCM) for comparison. The algorithms were tested on 33 images; for most images, the proposed method and FCM obtain similar overall scores, within one percent of each other. However, in about 18% of the cases, there is a signif…
Hierarchies of Self-Organizing Maps for action recognition
2016
We propose a hierarchical neural architecture able to recognise observed human actions. Each layer in the architecture represents increasingly complex human activity features. The first layer consists of a SOM which performs dimensionality reduction and clustering of the feature space. It represents the dynamics of the stream of posture frames in action sequences as activity trajectories over time. The second layer in the hierarchy consists of another SOM which clusters the activity trajectories of the first-layer SOM and learns to represent action prototypes. The third - and last - layer of the hierarchy consists of a neural network that learns to label action prototypes of the second-laye…
Studying the feasibility of a recommender in a citizen web portal based on user modeling and clustering algorithms
2006
This paper presents a methodology to estimate the future success of a collaborative recommender in a citizen web portal. This methodology consists of four stages, three of them are developed in this study. First of all, a user model, which takes into account some usual characteristics of web data, is developed to produce artificial data sets. These data sets are used to carry out a clustering algorithm comparison in the second stage of our approach. This comparison provides information about the suitability of each algorithm in different scenarios. The benchmarked clustering algorithms are the ones that are most commonly used in the literature: c-Means, Fuzzy c-Means, a set of hierarchical …
A New SOM Initialization Algorithm for Nonvectorial Data
2008
Self Organizing Maps (SOMs) are widely used mapping and clustering algorithms family. It is also well known that the performances of the maps in terms of quality of result and learning speed are strongly dependent from the neuron weights initialization. This drawback is common to all the SOM algorithms, and critical for a new SOM algorithm, the Median SOM (M-SOM), developed in order to map datasets characterized by a dissimilarity matrix. In this paper an initialization technique of M-SOM is proposed and compared to the initialization techniques proposed in the original paper. The results show that the proposed initialization technique assures faster learning and better performance in terms…
Stable Automatic Unsupervised Segmentation of Retinal Vessels Using Self-Organizing Maps and a Modified Fuzzy C-Means Clustering
2011
In this paper an automatic unsupervised method for the segmentation of retinal vessels is proposed. Three features are extracted from the tested image. The features are scaled down by a factor of 2 and mapped into a Self-Organizing Map. A modified Fuzzy C-Means clustering algorithm is used to divide the neuron units of the map 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 its best matching unit in 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 DRIVE database shows accurate ex…
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…
Behavior Classification with Self-Organizing Maps
2001
We describe a method that applies Self-Organizing Maps for direct clustering of spatio-temporal data. We use the method to evaluate the behavior of RoboCup players. By training the Self-Organizing Map with player data we have the possibility to identify various clusters representing typical agent behavior patterns. Thus we can draw certain conclusions about their tactical behavior, using purely motion data, i.e. logfile information. In addition, we examine the player-ball interaction that give information about the players' technical capabilities.
Verbal fluency in school-aged Spanish children: analysis of clustering and switching organizational strategies, employing different semantic categori…
2021
La tarea de fluidez verbal (FV) es una medida de la flexibilidad cognitiva y la estrategia de búsqueda dentro del léxico y el tema semántico. En este trabajo, se probó el uso de estrategias organizativas, es decir, agrupación y cambio en la fluidez semántica y fonológica en niños españoles sanos divididos en dos grupos: el grupo 1 de niños más pequeños (de 8 a 9 años) y el grupo 2 de niños mayores (de 10 años de edad) –11) introducción de diferentes letras (F, A, S y P, M, R) y categorías semánticas (animales y comidas o bebidas). La fluidez semántica fue mayor que la fluidez fonológica en ambos grupos de edad. Además, los niños mayores mostraron un mejor desempeño de ambas fluencias que lo…
The sequencing of the complete genome of a Tomato black ring virus (TBRV) and of the RNA2 of three Grapevine chrome mosaic virus (GCMV) isolates from…
2014
The complete genome of a Tomato black ring virus isolate (TBRV-Mirs) (RNA1, 7,366 nt and RNA2, 4,640 nt) and the RNA2 sequences (4,437; 4,445; and 4,442 nts) of three Grapevine chrome mosaic virus isolates (GCMV-H6, -H15, and -H27) were determined. All RNAs contained a single open reading frame encoding polyproteins of 254 kDa (p1) and 149 kDa (p2) for TBRV-Mirs RNA1 and RNA2, respectively, and 146 kDa for GCMV RNA2. p1 of TBRV-Mirs showed the highest identity with TBRV-MJ (94 %), Beet ringspot virus (BRSV, 82 %), and Grapevine Anatolian ringspot virus (GARSV, 66 %), while p2 showed the highest identity with TBRV isolates MJ (89 %) and ED (85 %), followed by BRSV (65 %), GCMV (58 %), and GA…