A Comparative Agglomerative Hierarchical Clustering Method to Cluster Implemented Course

There are many clustering methods, such as hierarchical clustering method. Most of the approaches to the clustering of variables encountered in the literature are of hierarchical type. The great majority of hierarchical approaches to the clustering of variables are of agglomerative nature. The agglomerative hierarchical approach to clustering starts with each observation as its own cluster and then continually groups the observations into increasingly larger groups. Higher Learning Institution (HLI) provides training to introduce final-year students to the real working environment. In this research will use Euclidean single linkage and complete linkage. MATLAB and HCE 3.5 software will used to train data and cluster course implemented during industrial training. This study indicates that different method will create a different number of clusters.Comment: 6 pages, 10 figures, published on Journal of Computing, Volume 2, Issue 12, December 201

The volume and Chern-Simons invariant of a Dehn-filled manifold

학위논문 (박사)-- 서울대학교 대학원 : 자연과학대학 수리과학부, 2019. 2. 박종일.Based on the work of Neumann, Zickert gave a simplicial formula for computing the volume and Chern-Simons invariant of a boundary-parabolic \psl-representation of a compact 3-manifold with non-empty boundary. Main aim of this thesis is to introduce a notion of deformed Ptolemy assignments (or varieties) and generalize the formula of Zickert to a representation of a Dehn-filled manifold. We also generalize the potential function of Cho and Murakami by applying our formula to an octahedral decomposition of a link complement in the 3-sphere. Also, motivated from the work of Hikami and Inoue, we clarify the relation between Ptolemy assignments and cluster variables when a link is given in a braid position. The last work is a joint work with Jinseok Cho and Christian Zickert.1 Introduction 1 1.1 Deformed Ptolemy assignments . . . . . . . . . . . . . . . . . . . 1 1.1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2 Potential functions . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3 Cluster variables . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 Preliminaries 12 2.1 Cocycles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 2.2 Obstruction classes . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3 Ptolemy varieties 16 3.1 Formulas of Neumann . . . . . . . . . . . . . . . . . . . . . . . . 16 3.2 Deformed Ptolemy varieties . . . . . . . . . . . . . . . . . . . . . 19 3.2.1 Isomorphisms . . . . . . . . . . . . . . . . . . . . . . . . . 23 3.2.2 Pseudo-developing maps . . . . . . . . . . . . . . . . . . . 27 3.3 Flattenings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.3.1 Main theorem . . . . . . . . . . . . . . . . . . . . . . . . . 36 4 Potential functions 43 4.1 Generalized potential functions . . . . . . . . . . . . . . . . . . . 43 4.1.1 Proof of Theorem 4.1.1 . . . . . . . . . . . . . . . . . . . 45 4.2 Relation with a Ptolemy assignment . . . . . . . . . . . . . . . . 50 4.2.1 Proof of Theorem 4.2.1 . . . . . . . . . . . . . . . . . . . 54 4.3 Complex volume formula . . . . . . . . . . . . . . . . . . . . . . . 57 4.3.1 Proof of Theorem 4.3.1 . . . . . . . . . . . . . . . . . . . 59 5 Cluster variables 70 5.1 The Hikami-Inoue cluster variables . . . . . . . . . . . . . . . . . 70 5.1.1 The octahedral decomposition . . . . . . . . . . . . . . . 70 5.1.2 The Hikami-Inoue cluster variables . . . . . . . . . . . . . 71 5.1.3 The obstruction cocycle . . . . . . . . . . . . . . . . . . . 74 5.1.4 Proof of Theorem 1.3.2 . . . . . . . . . . . . . . . . . . . 75 5.2 The existence of a non-degenerate solution . . . . . . . . . . . . . 79 5.2.1 Proof of Proposition 5.2.1 . . . . . . . . . . . . . . . . . . 81 5.2.2 Explicit computation from a representation . . . . . . . . 83Docto

A Bibliometric Analysis of Health Cloud Scientific\u27s Productions

Introduction: Cloud computing is an innovative paradigm meeting the user\u27s demand for accessing a shared source comprising adjustable computational sources, such as servers and applied programs. An increase in the costs of information technology, emerging problems with updating software and hardware, and expanded storage volume, make it possible to utilize cloud-based health information cases. Organizations have focused on cloud platform-based services as a new opportunity to develop the software industry for healthcare. The aim of the research is to conduct a bibliometric study of the scientific productions on health cloud . Methodology: The present study, applied in nature, was conducted using a bibliometric and scientometric method. It was conducted in 2018 using PubMed and key portmanteaus over the period 2009-2018. Subjected to the application of input and output standards, 491 research papers were selected for analysis. Findings: The findings revealed that the production of health cloud-focused papers over a decade, excluding those in 2017, had an upward trend. The US, India, and China were the most productive in this respect. Having presented 5 papers on cloud computing, Costa, Lee, Malamateniou, Stoicu-Tivadar, Vassilacopoulos, writers, were most productive. The greatest co-occurrence was that of the words Internet, electronic health records, computer security, information storage and retrieval, algorithms, confidentiality, female, male, delivery of health care, computer communication networks, medical informatics, mobile applications, data mining, and health information exchang. Conclusion: The results of the present study indicate the leading status of the USA in health cloud publications. In view of the recognition received for using cloud computing, the trend of the papers in the base was upward in nature. On analysis of the co-occurrence of words, the largest cluster was that of cloud computing with 6 items focused on: The Internet of Things (IoT), Electronic health record, healthcare, and e-health in one cluster, indicating the continuity of the issues