Aplicación del algoritmo K-Medoid para la segmentación de los alumnos ingresantes de una universidad.

Actualmente, en el área de educación superior se ha vuelto indispensable la gestión de los datos para la toma de decisiones académicas y la mejora de los procesos educativos, para ello la analítica y estadística han sido llevados al ámbito tecnológico, donde prima la automatización de procesos y la gestión de grandes bases de datos a través de algoritmos de Machine Learning, uno de los más utilizados son los algoritmos clustering, cuyo propósito es agrupar datos por similitud. El presente estudio tuvo como objetivo encontrar tipos de estudiantes universitarios respecto a sus variables sociodemográficas, económicas y de rendimiento académico, utilizando el algoritmo K-medoid en datos de alumnos ingresantes a la Universidad Nacional Agraria La Molina de Lima, Perú. Se pudo determinar que los ingresantes en estudio se pueden segmentar en 3 grupos, cada uno con características propias, lo que permitirá impulsar cambios a favor de la calidad educativa y promover la renovación de los espacios de enseñanza de manera personalizada en torno al tipo de estudiante que la universidad gestiona.Currently, in the area within higher education, data management has become essential for academic decision making and the improvement of educational processes. Analytics and statistics have been taken to the technological field, where the processes automation and the large databases management through Machine Learning algorithms are the most used, among which are the clustering algorithms, whose purpose is to group data by similarity. The objective of this study was to find types of university students with respect to their sociodemographic, economic and academic performance variables, using the K-medoid algorithm on data of students entering the Universidad Nacional Agraria La Molina in Lima, Peru. It was determined that the students under study can be segmented into 3 groups, each with its own characteristics, which will make it possibleto promote changes in favor of educational quality and promote the renovation of teaching spaces in a personalized way around the type of student that the university manages

Aplicación del algoritmo K-Medoid para la segmentación de los alumnos ingresantes de una universidad.

Actualmente, en el área de educación superior se ha vuelto indispensable la gestión de los datos para la toma de decisiones académicas y la mejora de los procesos educativos, para ello la analítica y estadística han sido llevados al ámbito tecnológico, donde prima la automatización de procesos y la gestión de grandes bases de datos a través de algoritmos de Machine Learning, uno de los más utilizados son los algoritmos clustering, cuyo propósito es agrupar datos por similitud. El presente estudio tuvo como objetivo encontrar tipos de estudiantes universitarios respecto a sus variables sociodemográficas, económicas y de rendimiento académico, utilizando el algoritmo K-medoid en datos de alumnos ingresantes a la Universidad Nacional Agraria La Molina de Lima, Perú. Se pudo determinar que los ingresantes en estudio se pueden segmentar en 3 grupos, cada uno con características propias, lo que permitirá impulsar cambios a favor de la calidad educativa y promover la renovación de los espacios de enseñanza de manera personalizada en torno al tipo de estudiante que la universidad gestiona.Currently, in the area within higher education, data management has become essential for academic decision making and the improvement of educational processes. Analytics and statistics have been taken to the technological field, where the processes automation and the large databases management through Machine Learning algorithms are the most used, among which are the clustering algorithms, whose purpose is to group data by similarity. The objective of this study was to find types of university students with respect to their sociodemographic, economic and academic performance variables, using the K-medoid algorithm on data of students entering the Universidad Nacional Agraria La Molina in Lima, Peru. It was determined that the students under study can be segmented into 3 groups, each with its own characteristics, which will make it possibleto promote changes in favor of educational quality and promote the renovation of teaching spaces in a personalized way around the type of student that the university manages

Variable neighbourhood search based heuristic for K-harmonic means clustering

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Although there has been a rapid development of technology and increase of computation speeds, most of the real-world optimization problems still cannot be solved in a reasonable time. Some times it is impossible for them to be optimally solved, as there are many instances of real problems which cannot be addressed by computers at their present speed. In such cases, the heuristic approach can be used. Heuristic research has been used by many researchers to supply this need. It gives a sufficient solution in reasonable time. The clustering problem is one example of this, formed in many applications. In this thesis, I suggest a Variable Neighbourhood Search (VNS) to improve a recent clustering local search called K-Harmonic Means (KHM).Many experiments are presented to show the strength of my code compared with some algorithms from the literature. Some counter-examples are introduced to show that KHM may degenerate entirely, in either one or more runs. Furthermore, it degenerates and then stops in some familiar datasets, which significantly affects the final solution. Hence, I present a removing degeneracy code for KHM. I also apply VNS to improve the code of KHM after removing the evidence of degeneracy

Some new techniques for pattern recognition research and lung sound signal analysis

This thesis describes the results of a collaborative research programme between the Department of Electronics & Electrical Engineering, University of Glasgow, and the Centre for Respiratory Investigation, Glasgow Royal Infirmary. The research was initially aimed at studying lung sound using signal processing and pattern recognition techniques. The use of pattern recogntion techniques was largely confined to exploratory data analysis which led to an interest in the methods themselves. A study was carried out to apply recent research in computational geometry to clustering Two geometric structures, the Gabriel graph and the relative neighbourhood graph, are both defined by a region of influence. A generalization of these graphs is used to find the conditions under which graphs defined by a region of influence are connected and planar. The Gabriel graph may be considered to be just planar and the relative neighbourhood graph to be just connected. From this two variable regions of influence were defined that were aimed at producing disconnected graphs and hence a partitioning of the data set, A hierarchic clustering based on relative distance may be generated by varying the size of the region of influence. The value of the clustering method is examined in terms of admissibility criteria and by a case study. An interactive display to complement the graph theoretical clustering was also developed. This display allows a partition in the clustering to be examined. The relationship between clusters in the partition may be studied by using the partition to define a contracted graph which is then displayed. Subgraphs of the original graph may be used to provide displays of individual clusterso This display should provide additional information about a partition and hence allow the user to understand the data better. The remainder of the work in this thesis concerns the application of pattern recogntition techniques to the analysis of lung sound signals. Breath sound was analysed using frequency domain methods since it is basically a continuous signal. Initially, a rather ad hoc method was used for feature extraction which was based on a piecewise constant approximation to the amplitude spectrum. While this method provided a useful set of features, it is clear that more systematic methods are required. These methods were used to study lung sound in four groups of patients: (1) normal patients, (2) patients with asbestosis, (3) patients with cryptogenic fibrosing alveolitis (CFA) and (4) patients with interstitial pulmonary oedema. The data sets were analysed using principal components analysis and the new graph theroretical clustering method (this data was used as a case study for the clustering method). Three groups of patients could be identified from the data;- (a) normal subjects, (b) patients with fibrosis of the lungs (asbestosis & CFA) and (c) patients with pulmonary oedema. These results suggest that lung sound may be able to make a useful contribution to non-invasive diagnosis. However more extensive studies are required before the real value of lung sound in diagnosis is established