Fuzzy Principal Components Analysis

Due to the large number of characteristics, there is a need to extract the most relevant characteristicsfrom the input data, so that the amount of information lost in this way is minimal, and the classification realized with the projected data set is relevant with respect to the original data. In order to achieve this feature extraction, different statistical techniques, as well as the principal components analysis (PCA) may be used. This thesis describes an extension of principal components analysis (PCA) allowing the extraction ofa finite number of relevant features from high-dimensional fuzzy data and noisy data. PCA finds linear combinations of the original measurement variables that describe the significant variation in the data. The comparisonof the two proposed methods was produced by using postoperative patient data. Experiment results demonstrate the ability of using the proposed two methods in complex data. Fuzzy PCA was used in the classificationproblem. The classification was applied by using the similarity classifier algorithm where total similarity measures weights are optimized with differential evolution algorithm. This thesis presents the comparison of the classification results based on the obtained data from the fuzzy PCA.

Evaluating multidimensional visualization techniques in data mining tasks

Visual data mining (VDM) tools employ information visualization techniques in order to represent large amounts of high-dimensional data graphically and to involve the user in exploring data at different levels of detail. The users are looking for outliers, patterns and models – in the form of clusters, classes, trends, and relationships – in different categories of data, i.e., financial, business information, etc. The focus of this thesis is the evaluation of multidimensional visualization techniques, especially from the business user’s perspective. We address three research problems. The first problem is the evaluation of projection-based visualizations with respect to their effectiveness in preserving the original distances between data points and the clustering structure of the data. In this respect, we propose the use of existing clustering validity measures. We illustrate their usefulness in evaluating five visualization techniques: Principal Components Analysis (PCA), Sammon’s Mapping, Self-Organizing Map (SOM), Radial Coordinate Visualization and Star Coordinates. The second problem is concerned with evaluating different visualization techniques as to their effectiveness in visual data mining of business data. For this purpose, we propose an inquiry evaluation technique and conduct the evaluation of nine visualization techniques. The visualizations under evaluation are Multiple Line Graphs, Permutation Matrix, Survey Plot, Scatter Plot Matrix, Parallel Coordinates, Treemap, PCA, Sammon’s Mapping and the SOM. The third problem is the evaluation of quality of use of VDM tools. We provide a conceptual framework for evaluating the quality of use of VDM tools and apply it to the evaluation of the SOM. In the evaluation, we use an inquiry technique for which we developed a questionnaire based on the proposed framework. The contributions of the thesis consist of three new evaluation techniques and the results obtained by applying these evaluation techniques. The thesis provides a systematic approach to evaluation of various visualization techniques. In this respect, first, we performed and described the evaluations in a systematic way, highlighting the evaluation activities, and their inputs and outputs. Secondly, we integrated the evaluation studies in the broad framework of usability evaluation. The results of the evaluations are intended to help developers and researchers of visualization systems to select appropriate visualization techniques in specific situations. The results of the evaluations also contribute to the understanding of the strengths and limitations of the visualization techniques evaluated and further to the improvement of these techniques.

Corporate Entrepreneurship in Healthcare Organizations: An explorative study of employees’ corporate entrepreneurial behavior, job satisfaction and turnover intention

The Finnish healthcare industry is currently facing significant challenges due to economic crises, aging population and major structural reforms, which have resulted in decreased job satisfaction and increased levels of turnover. This proposes that healthcare organizations need to come up with new, creative means to tackle these issues. Several researchers have argued that corporate entrepreneurship may be the necessary means to achieve this. As previous research has mainly focused on examining this concept from organizational perspective, this study looks at how it occurs on the level of individual employees. The purpose of this study is to examine how corporate entrepreneurship is manifested in individual behavior, and how this type of behavior is associated with the individual’s job satisfaction and turnover intention. Additionally, this study will examine the differences in corporate entrepreneurial behavior between private and public sector organizations, as previous research suggests that these two may be characterized differently. Data was collected with the help of a literature review as well as a survey study, which was sent out to a number of employees of four different healthcare organizations, out of which three were public and one was a private sector organization. Six distinct behavioral characteristics were recognized in previous research, which make up the measure for corporate entrepreneurial behavior. Principal components were formed from the different areas of the survey (corporate entrepreneurial behavior, job satisfaction, turnover intention), after which the association of these components were examined with linear regression analysis, which proved that corporate entrepreneurial behavior is positively correlated with both job satisfaction and intention to leave the organization. Differences between sectors were analyzed with analysis of variance and cross tabulation analysis, but neither of these suggested that any significant differences would occur. These results suggest that employees who behave entrepreneurially tend to be more satisfied with their jobs, but also consider leaving their current organizations more often than others. This may be due to the fact that healthcare organizations are not fertile for entrepreneurial behavior, which will drive entrepreneurial individuals looking for employers who may be more supportive of this type of behavior. With growing levels of dissatisfaction as well as little room for entrepreneurial behavior, the studied organizations may actually be in the process of losing those employees who have the ability and desire to behave in such manner, and who could very well be those who will eventually come up with solutions for the major challenges that these organizations are facing.