Search results for "Organizing"
showing 10 items of 113 documents
Similarity and Consistency in Hotel Online Ratings across Platforms
2019
Online ratings are a major driver of hotel choice. There are many ratings platforms, and the number of evaluations is huge. This article analyzes if hotel ratings vary across platforms, vary over time, and if consistency in ratings can be observed. Longitudinal online ratings taken from 11 platforms over a two-year period were analyzed through Self-Organizing Maps. The findings suggest a similar pattern of online ratings across most of the platforms, except for Yelp and HolidayCheck. In addition, the evaluation patterns are stable over time, and the analyzed attributes do not contribute decisively to explain the overall evaluation of hotels, which implies that tourists use a noncompensator…
A multiscale approach to automatic and unsupervised retinal vessel segmentation using Self-Organizing Maps
2016
In this paper an automatic unsupervised method for retinal vessel segmentation is described. Self-Organizing Map, modified Fuzzy C-Means, STAPLE algorithms and majority voting strategy were adopted to identify a segmentation of the retinal vessels. The performance of the proposed method was evaluated on the DRIVE database.
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…
Visualization of tonal content with self-organizing maps and self-similarity matrices
2005
This article presents a dynamic model of tonality perception based on a short-term memory model and a self-organizing map (SOM). The model can be used for dynamic visualization of perceived tonal content, making it possible to examine the clarity and locus of tonality at any given point of time. This article also presents a method for the visualization of tonal structure using self-similarity matrices. The methods are applied to compositions of J. S. Bach, S. Barber, and J. Pachelbel. Finally, a real-time application embracing the tonality perception model is presented.
Forecasting daily urban electric load profiles using artificial neural networks
2004
The paper illustrates a combined approach based on unsupervised and supervised neural networks for the electric energy demand forecasting of a suburban area with a prediction time of 24 h. A preventive classification of the historical load data is performed during the unsupervised stage by means of a Kohonen's self organizing map (SOM). The actual forecast is obtained using a two layered feed forward neural network, trained with the back propagation with momentum learning algorithm. In order to investigate the influence of climate variability on the electricity consumption, the neural network is trained using weather data (temperature, relative humidity, global solar radiation) along with h…
Fostering Teacher-Student Interaction and Learner Autonomy by the I-TUTOR Maps
2014
The paper analyses the use of an automatically generated map as a mediator; that map visually represents the study domain of a university course and fosters the co-activity between teachers and stu- dents. In our approach the role of the teacher is meant as a media- tor between the student and knowledge. The mediation (and not the transmission) highlights a process in which theres no deterministic rela- tion between teaching and learning. Learning is affected by the students previous experiences, their own modalities of acquisition and by the in- puts coming from the environment. The learning path develops when the teachers and the students visions approach and, partly, overlap. In this cas…
A neural network approach to movement pattern analysis.
2004
Movements are time-dependent processes and so can be modelled by time-series of coordinates: E.g., each articulation has geometric coordinates; the set of the coordinates of the relevant articulations build a high-dimensional configuration. These configurations--or "patterns"--give reason for analysing movements by means of neural networks: The Kohonen Feature Map (KFM) is a special type of neural network, which (after having been coined by training with appropriate pattern samples) is able to recognize single patterns as members of pattern clusters. This way, for example, the particular configurations of a given movement can be identified as belonging to respective configuration clusters, …
Visual Data Mining With Self-organizing Maps for “Self-monitoring” Data Analysis
2016
Data collected in psychological studies are mainly characterized by containing a large number of variables (multidimensional data sets). Analyzing multidimensional data can be a difficult task, especially if only classical approaches are used (hypothesis tests, analyses of variance, linear models, etc.). Regarding multidimensional models, visual techniques play an important role because they can show the relationships among variables in a data set. Parallel coordinates and Chernoff faces are good examples of this. This article presents self-organizing maps (SOM), a multivariate visual data mining technique used to provide global visualizations of all the data. This technique is presented as…
Tree Structured Self-Organizing Maps
1999
Publisher Summary This chapter provides an overview of the tree structured self-organizing maps (TS-SOM). It was originally intended as a fast implementation of the self-organizing map (SOM). The chapter explains that TS-SOM is a constructive smoother for a class of dimension reduction problems. There is a well known relation between self-organizing maps and principal curves. Unfortunately in most presentations it is derived by simple reasoning, avoiding the mathematical statement of the problem, which is essential to understand how efficient SOM implementations can be constructed. In this chapter, SOM is derived as a numerical solution of a generic model in a continuous domain, which diffe…
A Parallel Implementation of the Tree-Structured Self-Organizing Map
2002
This paper presents how Self-Organizing Maps (SOMs)can be trained efficiently using several, simultaneously executing threads on a shared memory Symmetric MultiProcessing (SMP)computer. The training method is a batch version of the Tree-Structured Self-Organizing Map. We note that SMP type of parallel training is very useful for large data sets obtained from nature, the process industry or large document collections, since we do not encounter similar model size limitations as with hardware SOM implementations.