Search results for "Self organizing map"
showing 6 items of 6 documents
Self organizing maps as a novel tool for data analysis in education
2016
Young people currently live and are connected to the virtual world in a natural and simple way. Nevertheless, in spite of the great advantages of the use of Information and Communication Technology, and particularly social networks, there are several drawbacks, principally security and privacy of net users. However, human behaviour is strongly non-linear, so usual statistical analysis does not yield accurate results. Now, machine learning algorithms are very common in solving real life non-linear problems, such as economics, medicine and engineering. So it would be worthy to apply this methodology on education data sets. In this work, a non-linear, visual algorithm named Self Organizing Map…
Simulated Annealing Technique for Fast Learning of SOM Networks
2011
The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for multidimensional input datasets. In this paper, we present an application of the simulated annealing procedure to the SOM learning algorithm with the aim to obtain a fast learning and better performances in terms of quantization error. The proposed learning algorithm is called Fast Learning Self-Organized Map, and it does not affect the easiness of the basic learning algorithm of the standard SOM. The proposed learning algorithm also improves the quality of resulting maps by providing better clustering quality and topology preservation of input multi-dimensi…
Multivariate statistical analysis of a large odorants database aimed at revealing similarities and links between odorants and odors
2017
International audience; The perception of odor is an important component of smell; the first step of odor detection, and the discrimination of structurally diverse odorants depends on their interactions with olfactory receptors (ORs). Indeed, the perception of an odor's quality results from a combinatorial coding, in which the deciphering remains a major challenge. Several studies have successfully established links between odors and odorants by categorizing and classifying data. Hence, the categorization of odors appears to be a promising way to manage odors. In the proposed study, we performed a computational analysis using odor descriptions of the odorants present in Flavor-Base 9th Edit…
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…
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…
The BioDICE Taverna plugin for clustering and visualization of biological data: a workflow for molecular compounds exploration
2014
Background: In many experimental pipelines, clustering of multidimensional biological datasets is used to detect hidden structures in unlabelled input data. Taverna is a popular workflow management system that is used to design and execute scientific workflows and aid in silico experimentation. The availability of fast unsupervised methods for clustering and visualization in the Taverna platform is important to support a data-driven scientific discovery in complex and explorative bioinformatics applications. Results: This work presents a Taverna plugin, the Biological Data Interactive Clustering Explorer (BioDICE), that performs clustering of high-dimensional biological data and provides a …