Multi-dimensional clustering in user profiling

User profiling has attracted an enormous number of technological methods and applications. With the increasing amount of products and services, user profiling has created opportunities to catch the attention of the user as well as achieving high user satisfaction. To provide the user what she/he wants, when and how, depends largely on understanding them. The user profile is the representation of the user and holds the information about the user. These profiles are the outcome of the user profiling. Personalization is the adaptation of the services to meet the user’s needs and expectations. Therefore, the knowledge about the user leads to a personalized user experience. In user profiling applications the major challenge is to build and handle user profiles. In the literature there are two main user profiling methods, collaborative and the content-based. Apart from these traditional profiling methods, a number of classification and clustering algorithms have been used to classify user related information to create user profiles. However, the profiling, achieved through these works, is lacking in terms of accuracy. This is because, all information within the profile has the same influence during the profiling even though some are irrelevant user information. In this thesis, a primary aim is to provide an insight into the concept of user profiling. For this purpose a comprehensive background study of the literature was conducted and summarized in this thesis. Furthermore, existing user profiling methods as well as the classification and clustering algorithms were investigated. Being one of the objectives of this study, the use of these algorithms for user profiling was examined. A number of classification and clustering algorithms, such as Bayesian Networks (BN) and Decision Trees (DTs) have been simulated using user profiles and their classification accuracy performances were evaluated. Additionally, a novel clustering algorithm for the user profiling, namely Multi-Dimensional Clustering (MDC), has been proposed. The MDC is a modified version of the Instance Based Learner (IBL) algorithm. In IBL every feature has an equal effect on the classification regardless of their relevance. MDC differs from the IBL by assigning weights to feature values to distinguish the effect of the features on clustering. Existing feature weighing methods, for instance Cross Category Feature (CCF), has also been investigated. In this thesis, three feature value weighting methods have been proposed for the MDC. These methods are; MDC weight method by Cross Clustering (MDC-CC), MDC weight method by Balanced Clustering (MDC-BC) and MDC weight method by changing the Lower-limit to Zero (MDC-LZ). All of these weighted MDC algorithms have been tested and evaluated. Additional simulations were carried out with existing weighted and non-weighted IBL algorithms (i.e. K-Star and Locally Weighted Learning (LWL)) in order to demonstrate the performance of the proposed methods. Furthermore, a real life scenario is implemented to show how the MDC can be used for the user profiling to improve personalized service provisioning in mobile environments. The experiments presented in this thesis were conducted by using user profile datasets that reflect the user’s personal information, preferences and interests. The simulations with existing classification and clustering algorithms (e.g. Bayesian Networks (BN), Naïve Bayesian (NB), Lazy learning of Bayesian Rules (LBR), Iterative Dichotomister 3 (Id3)) were performed on the WEKA (version 3.5.7) machine learning platform. WEKA serves as a workbench to work with a collection of popular learning schemes implemented in JAVA. In addition, the MDC-CC, MDC-BC and MDC-LZ have been implemented on NetBeans IDE 6.1 Beta as a JAVA application and MATLAB. Finally, the real life scenario is implemented as a Java Mobile Application (Java ME) on NetBeans IDE 7.1. All simulation results were evaluated based on the error rate and accuracy

Model to study the flow and use of knowledge in outsourced knowledge intensive projects: a multi-case study of three vaccine clinical trials in Latin America (countries researched: Colombia, Brazil and Mexico)

This thesis offers insights from knowledge management theory to understand the flow of knowledge across the multiple actors involved in the execution of a clinical trial in Latin America. In the last 12 years, the participation of Latin America in the business of clinical trials has significantly increased, becoming a highly demanded region to implement sponsored clinical research, overtaking regions like Africa, India, Southeast Asia, and Middle Eastern countries. Also, over this period, sponsors have increased the outsourcing of in-house activities such as trial monitoring, pharmacovigilance and regulatory services to Contracted Research Organisations (CROs), shifting the ‘two organisations’ and bi-directional relationship between the sponsor and the research sites. This change in the clinical trials landscape has also taken place in Latin America, where in addition to the CRO, the figure of Site Management Organisations (SMOs) has emerged to manage multiple research sites over the course of the trial. Therefore, the internationalisation of clinical research, plus the outsourcing of strategic activities, have transformed the implementation of clinical trials in the region. On the other hand, the results of a clinical trial depend strongly on the analytical skills and cognitive capabilities employed by people working on the project. These characteristics make the clinical trial a Knowledge Intensive Project (KIP), where the main project outcomes depend to a large extent on the use of knowledge by the workers, and the transfer of knowledge data and information across the multiple organisations working in the clinical trial. Because knowledge is the primary production factor in a clinical trial, and in the context of Latin America, to my knowledge, there is reduced research about the production of clinical evidence and the role of each one of the actors over the execution, the main research question that this thesis answers is: How does knowledge flow across organisations and is employed by people in their firms to implement the clinical trials and obtain their respective results? To answer this research question, I proposed and evaluated a three-step model to study the flow of knowledge, data and information across multiple organisations being part of the clinical trial and the use of these to produce the knowledge products by the sponsor and the research sites. This model has its roots in the literature of knowledge ‘models, work and processes’, the concept of interdependence and the literature of knowledge transfer and acquisition in the outsourced project. The model consists of three steps to address, at the inter-organisational level, the transfer and acquisition of knowledge, data and information and the interdependency on results; and at the intra-organisational level, the use of knowledge and storage. The presented model was evaluated and complemented based on the evidence collected through a multi-case study of three multi-organisational clinical trials to evaluate three new vaccine candidates in Colombia, Brazil and Mexico. The findings of this research indicated that the model was robust to study the flow of knowledge, data and information between the sponsor and the research sites, from the design of the protocol to the production of the clinical data. The results also indicate that the presence of intermediaries decreases the transfer of knowledge and information between the parts, and induces the selectivity of the research sites toward one of the sources of knowledge, the Sponsor, the CROs or SMO. The evidence shows that the acquisition of knowledge by physicians demands a knowledge-destruction capability to actively employ the acquired knowledge in the trial and the constant presence of loops to reinforce the knowledge acquisition. The empirical findings of knowledge and data acquisition by the research sites and the sponsor contributed to developing the concept of permeability, contributing to the literature of knowledge acquisition in outsourced projects. This research addresses, for the first time, the implementation of vaccine clinical trials in Latin America countries and the contribution of the local researcher to the project, especially with their knowledge about the communities intervened. But it also highlighted some of the aspects that affect the implementation of clinical trials, such as the labour conditions in academia, which induce turnover, and the lack of harmonisation among clinical trial regulation in the region. In conclusion, the model proposed allowed me to address simply the complexities that take place in the production of knowledge products in multi-organisational clinical trials in Latin America countries