Analysis of Agglomerative Clustering

The diameter $k$-clustering problem is the problem of partitioning a finite subset of $\mathbb{R}^d$ into $k$ subsets called clusters such that the maximum diameter of the clusters is minimized. One early clustering algorithm that computes a hierarchy of approximate solutions to this problem (for all values of $k$) is the agglomerative clustering algorithm with the complete linkage strategy. For decades, this algorithm has been widely used by practitioners. However, it is not well studied theoretically. In this paper, we analyze the agglomerative complete linkage clustering algorithm. Assuming that the dimension $d$ is a constant, we show that for any $k$ the solution computed by this algorithm is an $O(\log k)$-approximation to the diameter $k$-clustering problem. Our analysis does not only hold for the Euclidean distance but for any metric that is based on a norm. Furthermore, we analyze the closely related $k$-center and discrete $k$-center problem. For the corresponding agglomerative algorithms, we deduce an approximation factor of $O(\log k)$ as well.Comment: A preliminary version of this article appeared in Proceedings of the 28th International Symposium on Theoretical Aspects of Computer Science (STACS '11), March 2011, pp. 308-319. This article also appeared in Algorithmica. The final publication is available at http://link.springer.com/article/10.1007/s00453-012-9717-

Lecturers' vs. students' perceptions of the accessibility of instructional materials

The goal of this study was to examine the differences between lecturers and students’ perceptions of the accessibility of instructional materials. The perceptions of 12 mature computing distance education students and 12 computing lecturers were examined using the knowledge elicitation techniques of card sorting and laddering. The study showed that lecturers had pedagogical views while students tended to concentrate on surface attributes such as appearance. Students perceived instructional materials containing visual representations as most accessible. This has two implications for the professional development of computing lecturers designing instructional materials. First, lecturers need to appreciate the differences between expert and novice views of accessibility and how students will engage with the materials. Second, lecturers need to understand that learners perceive instructional materials containing visual representations as more accessible compared to ‘text only’ versions. Hence greater use of these may enable students to engage more readily in learning. Given that print is the ubiquitous teaching medium this is likely to have implications for students and lecturers in other disciplines

Cluster analysis

Trabajo de investigaciónSe desarrolló un Componente Web de Clustering para Análisis de Datos el cual permite ingresar mediante una interfaz web y/o una API un conjunto de datos que son analizados mediante técnicas de clustering, estas técnicas permiten realizar agrupamiento de datos basados en sus características, y así arrojar un resultado que relaciona dichos datos. Lo anterior, busca proveer a la Universidad de una herramienta que permita de manera general, práctica y gratuita, realizar análisis de datos basados en clustering para cualquier estudio de investigación.INTRODUCCIÓN 1. GENERALIDADES 2. OBJETIVOS DEL PROYECTO 3. MARCO REFERENCIAL 4. MARCO CONCEPTUAL 5. METODOLOGÍA 6. DESARROLLO DEL PROYECTO 7. RESULTADOS 8. CONCLUSIONES BIBLIOGRAFÍA ANEXOSPregradoIngeniero de Sistema