The purpose of this Masters thesis is to analyze the research front within medical ethics. We wish to identify which research themes that are included in the subject field of medical ethics. The research themes can be identified by a couple of bibliometrical methods and techniques. In our case, it is bibliographic coupling combined with a Cluster analysis. The analysis is accomplished by the extracted references from articles in the two journals Bioethics and Journal of medical ethics.

This paper aims to map and contrast the research front and theresearch base of Library and Information Science (LIS) inScandinavia during 1980-1989 and 2003-2013 from articlespublished in Web of Science.The research questions are:1. How is the research base and research front in LIS inScandinavia between 1980-1989 and 2003-2012 structured?2. What differences can be observed between the two time periods1980-1989 and 2003-2012 with regards to the research base?3.

The purpose of this thesis is to evaluate the combination of bibliographic coupling and three hierarchical cluster analytic techniques: average, complete and single linkage. Bibliographic coupling is applied as a measure of similarity between recently published articles and the cluster techniques are used for the partition of a set of articles into subsets of articles with similar research focus. The field of information science is applied as the test arena and the selection of data, i.e., the article population to be partitioned, is based on a number of central journals. Methods of evaluation include both quantitative approaches as well as a qualitative one. For comparing the resulting partitions, adjusted Rand index is utilized and for the establishment of cluster coherence and isolation, measures of average normalized coupling strength is applied.

The aim of this study is to analyze and describe the research front within information science. To reach this purpose we ask the following research question: Which research themes can be identified within the subject field of information science by using bibliographic coupling in combination with Cluster analysis.The data for our study consists of articles from the journals Journal of the American Society for Information Science and Technology and Journal of Documentation throughout the years 2004-2006. The bibliographic information from the articles was analyzed with the combined methods of bibliographic coupling and Cluster analysis. This gave fifteen clusters within which we identified research themes. When analyzing these results we found four main fields of research within the research front of information science: Information retrieval, bibliometrics, knowledge organization and information science as research discipline.

The main objective of this study is to divide a number of colorectal cancer cases into subgroups based on their molecular features using Cluster analysis. The data used is supplied by a research group at Pathology, the Department of Medical Biosciences, Umeå University, and consists, after some preparation, of 455 observations which is a larger data set than many similar studies. The molecular variables that the clustering is based on are CIMP (CpG Island Methylator Phenotype), MSI (Micro Satellite Instability), BRAF- and KRAS-mutations. These are categorical variables and consequently the clustering method used is PAM (Partitioning Around Medoids) which is particularly useful with data on diverse variable level. The final analysis results in four subgroups that are represented by different combinations of attributes on the aforementioned variables.

The main objective of this study is to divide a number of colorectal cancer cases into subgroups based on their molecular features using Cluster analysis. The data used is supplied by a research group at Pathology, the Department of Medical Biosciences, Umeå University, and consists, after some preparation, of 455 observations which is a larger data set than many similar studies. The molecular variables that the clustering is based on are CIMP (CpG Island Methylator Phenotype), MSI (Micro Satellite Instability), BRAF- and KRAS-mutations. These are categorical variables and consequently the clustering method used is PAM (Partitioning Around Medoids) which is particularly useful with data on diverse variable level. The final analysis results in four subgroups that are represented by different combinations of attributes on the aforementioned variables.

In Economic Geography, there has been a strong interest of how different forms of proximity impacts businesses. In addition, there is also a strong interest in business agglomeration, as a result businesses cluster together through physical proximity, networks and knowledge. In the development sector, there is a strong interest on how to best support the creations and growth of small enterprises.This essay emphasises on the business cluster formations in Katutura, a suburb to Windhoek the capital city of Namibia. It oversees the impacts of different types of cluster formations and their effects on the businesses. In addition, this essay will identify the main clusters in Katutura and state what makes them unique from each other and what is a common cluster feature.

The main objective of this study is to divide a number of colorectal cancer cases into subgroups based on their molecular features using Cluster analysis. The data used is supplied by a research group at Pathology, the Department of Medical Biosciences, Umeå University, and consists, after some preparation, of 455 observations which is a larger data set than many similar studies. The molecular variables that the clustering is based on are CIMP (CpG Island Methylator Phenotype), MSI (Micro Satellite Instability), BRAF- and KRAS-mutations. These are categorical variables and consequently the clustering method used is PAM (Partitioning Around Medoids) which is particularly useful with data on diverse variable level. The final analysis results in four subgroups that are represented by different combinations of attributes on the aforementioned variables.

The purpose of my study is to study Social Democrats immigration policy over the period 1990 and 2013. My material consists of party programs and motions that social democracy has raised in Parliament. As a research method I used McGee´s ideograph theory. I use a Cluster analysis inspired by Kenneth Burke to analyze the meaning of those found ideographs. I also present and use Bitzers rhetorical situation in my analyzeWith help of Burke´s rhetorical situation I could see how the Social Democrats' rhetorical approach to immigration policy in the different periods in society.My investigation of the Social Democratic Party program and motions / propositions revealed the following ideographs: Democracy, solidarity, equality and freedom.

The main objective of this study is to divide a number of colorectal cancer cases into subgroups based on their molecular features using Cluster analysis. The data used is supplied by a research group at Pathology, the Department of Medical Biosciences, Umeå University, and consists, after some preparation, of 455 observations which is a larger data set than many similar studies. The molecular variables that the clustering is based on are CIMP (CpG Island Methylator Phenotype), MSI (Micro Satellite Instability), BRAF- and KRAS-mutations. These are categorical variables and consequently the clustering method used is PAM (Partitioning Around Medoids) which is particularly useful with data on diverse variable level. The final analysis results in four subgroups that are represented by different combinations of attributes on the aforementioned variables.

The aim of this thesis is to examine one part of Amazon.coms recommender systems: Similar Items. The purpose is also to show the difficulties with subject access, i. e. to find a similar book to the one you just read; problems with traditional systems versus the usefulness with recommender systems like Amazon.coms Similar Items. Similar Items presents a set, a cluster of related items to a given item based on customers co-purchase.

Bees are of importance for the global food security. In Europe 84% and in the World 35% of all the agricultural crops are dependent of animal pollination (Winfree et al., 2011:1). In agricultural production there is a huge lack of pollinators (Azien & Harden, 2009). A problem that is widely spread. In Sweden the problem becomes clear when looking at the statistics of the number of bee colonies during the last 20 years, where it has been a decline with 63 % (Statistiska Centralbyrån & Jordbruksverket, 2011).

Statistics Sweden is an administrative agency that delimits built-up areas and produces statistics regarding them. The statistics provide information about the area of the built-up areas, their population number, number of gainfully employees working in the built-up areas, and of buildings. Now Statistics Sweden wishes to extend such statistics by producing a measure regarding how well developed the service is in each built-up area.This study is a contribution to this statistical improvement work and the purpose is to ? by employing geographical information systems and Cluster analysis ? classify the Swedish built-up areas according to 1) service width and 2) service degree. A particular built-up area has a high service width if it has many different service functions, such as pharmacies, schools and grocery stores.

The snow leopard (Panthera uncia) is an elusive felid, native to the mountains in central Asia. Basic knowledge about the snow leopards? ecology has long been lacking but is advancing with the help of the GPS-technology. GPS Cluster analysis can provide insight in the diet and prey selection of elusive predators, such as the snow leopard. Acceleration data from GPS collars can be used to study animal behavior but the two have never been combined to gain more detailed information of the feeding behavior of large carnivores.

Automatic query expansion has long been studied in information retrieval research as a technique that deals with the fundamental issue of word mismatch between query and document. The purpose of this thesis is to compare the retrieval effectiveness of different strategies for automatic query expansion. The strategies are based on local analysis of the corpus and use statistical information from the local document set to extract terms that suppose to adapt themselves to each individual search and therefore appear to be searchonyms to the index terms. The strategies compared are: association clusters, metric cluster and scalar cluster. Baseline queries of 24 topics are expanded using terms from the different clusters and searches are made.