In this paper we evaluate the performance of a Java library in the context of designing numerical solutions for linear algebra problems. Nowadays there are a number of libraries like LAPACK or ATLAS available in C or FORTRAN for solving such problems. Java is a relatively new object-oriented language and is almost universally recognized as a very good programming language for writing portable programs. Despite these advantages, it is a common believe that Java still lags behind C/C++ or Fortran performance especially for computational intensive numerical applications. We developed a Java library for matrix computations and show that using a set of optimization techniques Java can achieve performance comparable with other libraries developed in C or Fortran.
The aim of this chapter is to explore the application of data mining for analyzing performance and satisfaction of the students enrolled in an online two-year master degree programme in project management. This programme is delivered by the Academy of Economic Studies, the biggest Romanian university in economics and business administration in parallel, as an online programme and as a traditional one. The main data sources for the mining process are the survey made for gathering students’ opinions, the operational database with the students’ records and data regarding students activities recorded by the e-learning platform are. More than 180 students have responded, and more than 150 distinct characteristics/ variable per student were identified. Due the large number of variables data mining is a recommended approach to analysis this data. Clustering, classification, and association rules were employed in order to identify the factor explaining students’ performance and satisfaction, and the relationship between them. The results are very encouraging and suggest several future developments.
Large multivariate data sets can prove difficult to comprehend, and hardly allow the observer to figure out the pattern structures, relationships and trends existing in samples and justifies the efforts of finding suitable methods from extracting relevant information from data. In our approach, we consider a probabilistic class model where each class H h ∈ is represented by a probability density function defined on n R ; where n is the dimension of input data and H stands for a given finite set of classes. The classes are learned by the algorithm using the information contained by samples randomly generated from them. The learning process is based on the set of class skeletons, where the class skeleton is represented by the principal axes estimated from data. Basically, for each new sample, the recognition algorithm classifies it in the class whose skeleton is the "nearest" to this example. For each new sample allotted to a class, the class characteristics are recomputed using a first order approximation technique. Experimentally derived conclusions concerning the performance of the new proposed method are reported in the final section of the paper.
The aim of the research reported in the paper was twofold: to propose a new approach in cluster analysis and to investigate its performance, when it is combined with dimensionality reduction schemes. Our attempt is based on group skeletons defined by a set of orthogonal and unitary eigen vectors (principal directions) of the sample covariance matrix. Our developments impose a set of quite natural working assumptions on the true but unknown nature of the class system. The search process for the optimal clusters approximating the unknown classes towards getting homogenous groups, where the homogeneity is defined in terms of the "typicality" of components with respect to the current skeleton. Our method is described in the third section of the paper. The compression scheme was set in terms of the principal directions corresponding to the available cloud. The final section presents the results of the tests aiming the comparison between the performances of our method and the standard k-means clustering technique when they are applied to the initial space as well as to compressed data.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.