Gintautas Dzemyda

Visualization of a set of parameters characterized by their correlation matrix

Abstract An approach to visualization of a set of parameters characterized by their correlation matrix has been proposed. It integrates two methods for data mapping: Sammons mapping and self-organizing map (SOM). They are based on different principles, and, therefore, supplement each other when they are used jointly. It is shown experimentally that some (sometimes sufficient) knowledge on a set of parameters may be obtained by using individual methods. However, in most cases the necessity and quality of their joint use is unquestionable – this allows us to observe the same data set from various standpoints and to extend our knowledge on the object of investigation.

Web-based Biometric Computer Mouse Advisory System to Analyze a User's Emotions and Work Productivity

This chapter describes the analysis of emotional state and work productivity using a Web-based Biometric Computer Mouse Advisory System to Analyze a User’s Emotions and Work Productivity (Advisory system hereafter) developed by author in conjunction with colleagues. The Advisory system determines the level of emotional state and work productivity integrally by employing three main biometric techniques (physiological, psychological and behavioral). By using these three biometric techniques, the Advisory system can analyze a person’s eleven states of being (stress, work productivity, mood, interest in work) and seven emotions (self-control, happiness, anger, fear, sadness, surprise and anxiety) during a realistic timeframe. Furthermore, to raise the reliability of the Advisory system even more, it also integrated the data supplied by the Biometric Finger (blood pressure and pulse rates). Worldwide research includes various scientists who conducted in-depth studies on the different and very important areas of biometric mouse systems. However, biometric mouse systems cannot generate recommendations. The Advisory system determines a user’s physiological, psychological and behavioral/movement parameters based on that user’s real-time needs and existing situation. It then generates thousands of alternative stress management recommendations based on the compiled Maslow’s Pyramid Tables and selects out the most rational of these for the user’s specific situation. The information compiled for Maslow’s Pyramid Tables consists of a collection of respondent surveys and analyses of the best global practices. Maslow’s Pyramid Tables were developed for an employee working with a computer in a typical organization. The Advisory system provides a user with a real-time assessment of his/her own productivity and emotional state. This chapter presents the Advisory system, a case study and a scenario used to test and validate the developed Advisory system and its composite parts to demonstrate its validity, efficiency and usefulness.

Optimal decisions in combining the SOM with nonlinear projection methods

Abstract Visual data mining is an efficient way to involve human in search for a optimal decision. This paper focuses on the optimization of the visual presentation of multidimensional data. A variety of methods for projection of multidimensional data on the plane have been developed. At present, a tendency of their joint use is observed. In this paper, two consequent combinations of the self-organizing map (SOM) with two other well-known nonlinear projection methods are examined theoretically and experimentally. These two methods are: Sammon’s mapping and multidimensional scaling (MDS). The investigations showed that the combinations (SOM_Sammon and SOM_MDS) have a similar efficiency. This grounds the possibility of application of the MDS with the SOM, because up to now in most researches SOM is applied together with Sammon’s mapping. The problems on the quality and accuracy of such combined visualization are discussed. Three criteria of different nature are selected for evaluation the efficiency of the combined mapping. The joint use of these criteria allows us to choose the best visualization result from some possible ones. Several different initialization ways for nonlinear mapping are examined, and a new one is suggested. A new approach to the SOM visualization is suggested. The obtained results allow us to make better decisions in optimizing the data visualization.

Heuristic approach for minimizing the projection error in the integrated mapping

Abstract In this paper, we have developed and examined a heuristic approach for minimizing the projection error in Sammon’s mapping applied in combination with the self-organizing map (SOM). As a final result, we need to visualize the neurons-winners of the SOM. The criterion of visualization quality is the projection error of Sammon’s mapping. Two combinations were considered: (1) a consecutive application of the SOM and Sammon’s mapping and (2) Sammon’s mapping taking into account the learning flow of the self-organizing neural network (integrated combination of the mapping methods). The goal is to obtain a lower projection error and its lower dependence on the so-called “magic factor” in Sammon’s mapping. Different modifications of Sammon’s mapping are examined experimentally and applied in the combination with the SOM. A parallel algorithm of the integrated combination has been proposed.

Multiple criteria decision support system: methods, user's interface and applications

A multiple criteria decision support system has been developed and implemented on the personal computer. Three interactive methods of increasing complexity are realized. The main applications of the system were in the scope of decisions on the best energy development strategy for Lithuania.

Recommended Biometric Stress Management System

Abstract The experiences of undergoing economic crises attest that the loss of employment prompts an outbreak of mental illnesses and suicides, increases the numbers of heart attacks and strokes and negatively affects other illnesses suffered by individuals under stress. Negative stress can devastate a person, cause depression, lower productivity on the job and the competitiveness of businesses and damage the quality of life. The Recommended Biometric Stress Management System, which the aforementioned authors of this article have developed, can assist in determining the level of negative stress and resolve the problem for lessening it. The system can help to manage current stressful situation and to minimise future stress by making the level of future need satisfaction more rational. In the first case, the system facilitates individuals to make a real-time assessment of their stress level and, after they fill in a stress management questionnaire, to get rational tips for the reduction of current stress based on the best global practice accumulated in the system. The multi-variant design and multiple criteria analysis methods are used for that purpose. The generation of recommendations and the selection of the most rational are based on criteria systems and on Maslow’s Hierarchy of Needs. Since this is an interdisciplinary area of research, psychologists, philosophers and experts in information management and decision-making theories and intelligent and biometric technologies participated in the development of this system. Over the course of this system’s development, the biometric technologies of information, intelligence and voice were integrated. The case study submitted in this article demonstrates this developed system.

Comparative Analysis of the Graphical Result Presentation in the SOM Software

In the paper, we analyze the software that realizes the self-organizing maps: SOM-PAK, SOM-TOOLBOX, Viscovery SOMine, Nenet, and two academic systems. Most of the software may be found in the Internet. These are freeware, shareware or demo. The self-organizing maps assist in data clustering and analyzing data similarities. The software differs one from another in the realization and visualization capabilities. The data on coastal dunes and their vegetation in Fin- land are used for the experimental comparison of the graphical result presentation of the software. Similarities of the systems and their differences, advantages and imperfections are exposed.

Optimization of the Local Search in the Training for SAMANN Neural Network

In this paper, we discuss the visualization of multidimensional data. A well-known procedure for mapping data from a high-dimensional space onto a lower-dimensional one is Sammon’s mapping. This algorithm preserves as well as possible all interpattern distances. We investigate an unsupervised backpropagation algorithm to train a multilayer feed-forward neural network (SAMANN) to perform the Sammon’s nonlinear projection. Sammon mapping has a disadvantage. It lacks generalization, which means that new points cannot be added to the obtained map without recalculating it. The SAMANN network offers the generalization ability of projecting new data, which is not present in the original Sammon’s projection algorithm. To save computation time without losing the mapping quality, we need to select optimal values of control parameters. In our research the emphasis is put on the optimization of the learning rate. The experiments are carried out both on artificial and real data. Two cases have been analyzed: (1) training of the SAMANN network with full data set, (2) retraining of the network when the new data points appear.

Large Datasets Visualization with Neural Network Using Clustered Training Data

This paper presents the visualization of large datasets with SAMANN algorithm using clustering methods for initial dataset reduction for the network training. The visualization of multidimensional data is highly important in data mining because recent applications produce large amount of data that need specific means for the knowledge discovery. One of the ways to visualize multidimensional dataset is to project it onto a plane. This paper analyzes the visualization of multidimensional data using feed-forward neural network. We investigate an unsupervised backpropagation algorithm to train a multilayer feed-forward neural network (SAMANN) to perform the Sammon`s nonlinear projection. The SAMANN network offers the generalization ability of projecting new data. Previous investigations showed that it is possible to train SAMANN using only a part of analyzed dataset without the loss of accuracy. It is very important to select proper vector subset for the neural network training. One of the ways to construct relevant training subset is to use clustering. This allows to speed up the visualization of large datasets.

Parallel Realizations of the SAMANN Algorithm

Sammons mapping is a well-known procedure for mapping data from a higher-dimensional space onto a lower-dimensional one. But the original algorithm has a disadvantage. It lacks generalization, which means that new points cannot be added to the obtained map without recalculating it. The SAMANN neural network, that realizes Sammons algorithm, provides a generalization capability of projecting new data. A drawback of using SAMANN is that the training process is extremely slow. One of the ways of speeding up the neural network training process is to use parallel computing. In this paper, we proposed some parallel realizations of the SAMANN.