Statistical segmentation methods and color variance analysis of retinal images

In this research, the effectiveness of Naive Bayes and Gaussian Mixture Models classifiers on segmenting exudates in retinal images is studied and the results are evaluated with metrics commonly used in medical imaging. Also, a color variation analysis of retinal images is carried out to find how effectively can retinal images be segmented using only the color information of the pixels.

Visual analytics for behavioral and niche market segmentation

Companies require information in order to gain an improved understanding of their customers. Data concerning customers, their interests and behavior are collected through different loyalty programs. The amount of data stored in company data bases has increased exponentially over the years and become difficult to handle. This research area is the subject of much current interest, not only in academia but also in practice, as is shown by several magazines and blogs that are covering topics on how to get to know your customers, Big Data, information visualization, and data warehousing. In this Ph.D. thesis, the Self-Organizing Map and two extensions of it – the Weighted Self-Organizing Map (WSOM) and the Self-Organizing Time Map (SOTM) – are used as data mining methods for extracting information from large amounts of customer data. The thesis focuses on how data mining methods can be used to model and analyze customer data in order to gain an overview of the customer base, as well as, for analyzing niche-markets. The thesis uses real world customer data to create models for customer profiling. Evaluation of the built models is performed by CRM experts from the retailing industry. The experts considered the information gained with help of the models to be valuable and useful for decision making and for making strategic planning for the future.

New ways to do business plans in multicultural organizations

Business plans are made when establishing new company or when organizations launch new product or services. In this Master Thesis was examined the elements are included in the business plan and emphasized. Business plan is a wide document and can also contain company specific information, the literature review was restricted into three areas which were investigated from the relating literature and articles. The selected areas were Market Segmentation and Targeting, Competitive Environment, and Market Positioning and Strategy. The different business plan models were investigated by interviewing companies who operates in a different industry sectors from each other’s. The models were compared to each other and to the findings from literature. Based on interview results and literature findings, the business plan for fibre based packaging. The created business plan contains three selected areas. It was found that the selected business plan elements can be found from the interviewed companies’ business plans. The market segmentation was done by comparing the market share to known total market size. When analyzing the competitive environment, there was no one selected model in use. The tools to evaluate competitive environment was selected parts from both SWOT analysis and Porter’s five forces model in applicable part. Based on interview results, it can be state that the company or organization should find and built its own model for business plans. In order to receive the benefits for future planning, the company should use the same model for long time.

Visual Customer Segmentation and Data Exploration with the Self-Organizing Map

Advancements in information technology have made it possible for organizations to gather and store vast amounts of data of their customers. Information stored in databases can be highly valuable for organizations. However, analyzing large databases has proven to be difficult in practice. For companies in the retail industry, customer intelligence can be used to identify profitable customers, their characteristics, and behavior. By clustering customers into homogeneous groups, companies can more effectively manage their customer base and target profitable customer segments. This thesis will study the use of the self-organizing map (SOM) as a method for analyzing large customer datasets, clustering customers, and discovering information about customer behavior. Aim of the thesis is to find out whether the SOM could be a practical tool for retail companies to analyze their customer data.