Virginijus Marcinkevičius

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.

Strategies for Big Data Clustering

In the paper, an overview of methods and technologies used for big data clustering is presented. The clustering is one of the important data mining issue especially for big data analysis, where large volume data should be grouped. Here some clustering methods are described, great attention is paid to the k-means method and its modifications, because it still remains one of the popular methods and is implemented in innovative technologies for big data analysis. Neural network-based self-organizing maps and their extensions for big data clustering are reviewed, too. Some strategies for big data clustering are also presented and discussed. It is shown the data of which volume can be clustered in the well known data mining systems WEKA and KNIME and when new sophisticated technologies are needed.

The Problem of Visual Analysis of Multidimensional Medical Data

We consider the problem of visual analysis of the multidimensional medical data. A frequent problem in medicine is an assignment of a health state to one of the known classes (for example, healthy or sick persons). A particularity of medical data classification is the fact that the transit from the normal state to diseased one is often not so conspicuous. From the table of the parametric medical multidimensional data, it is difficult to notice which objects are similar, which ones are different, i.e., which class they belong to. Therefore it is necessary to classify the multidimensional data by various classification methods. However, classification errors arc inevitable and the results of classification in medicine must be as correct as possible. That is why it is advisable to use different types of data analysis methods, for example, in addition to visualize the multidimensional data (to project to a plane). A visual analysis allows us to estimate similarities and differences of objects, a partial assignment to one or another class in simple visual way. However, the shortcoming of this analysis is the fact that while projecting multidimensional data to a plane, a part of the information is inevitably lost. Thus, one of the agreeable methods is a combination of classification and visualization methods. This synthesis lets us to obtain a more objective conclusions on the analysed data. The results, obtained by the integrated method, proposed in this chapter, can help medics to preliminary diagnose successfully or have some doubt on the former diagnosis.

Large-scale multidimensional data visualization: a web service for data mining

In this paper, we present an approach of the Web application (as a service) for data mining oriented to the multidimensional data visualization. The stress is put on visualization methods as a tool for the visual presentation of large-scale multidimensional data sets. The proposed implementation includes five visualization methods: MDS SMACOF algorithm, Relative MDS, Diagonal majorization algorithm, Relational perspective map, SAMANN. A cluster for parallel computation is used by Web service for the visual data mining. The service is of free access to the user community for data visualization.

Web Application for Large-Scale Multidimensional Data Visualization

Abstract In this paper, we present an approach of the web application (as a service) for data mining oriented to the multidimensional data visualization. This paper focuses on visualization methods as a tool for the visual presentation of large-scale multidimensional data sets. The proposed implementation of such a web application obtains a multidimensional data set and as a result produces a visualization of this data set. It also supports different configuration parameters of the data mining methods used. Parallel computation has been used in the proposed implementation to run the algorithms simultaneously on different computers.

A new web-based solution for modelling data mining processes

Abstract The conventional technologies and methods are not able to store and analyse recent data that come from different sources: various devices, sensors, networks, transactional applications, the web, and social media. Due to a complexity of data, data mining methods should be implemented using the capabilities of the Cloud technologies. In this paper, a new web-based solution named DAMIS, inspired by the Cloud, is proposed and implemented. It allows making massive data mining simpler, effective, and easily understandable for data scientists and business intelligence professionals by constructing scientific workflows for data mining using a drag and drop interface. The usage of scientific workflows allows composing convenient tools for modelling data mining processes and for simulation of real-world time- and resource-consuming data mining problems. The solution is useful to solve data classification, clustering, and dimensionality reduction problems. The DAMIS architecture is designed to ensure easy accessibility, usability, scalability, and portability of the solution. The proposed solution has a wide range of applications and allows to get deep insights into the data during the process of knowledge discovery.

Application of Logistic Regression with part-of-the-speech tagging for multi-class text classification

Today, computing environment provides the possibility of carrying out various data-intensive natural language processing tasks. Language tokenization methods applied for multi-class text classification are recently investigated by many data scientists. The authors of this paper investigate Logistic Regression method by evaluating classification accuracy which correlates on the size of the training data, POS and number of n-grams. Logistic Regression method is implemented in Apache Spark, the in-memory intensive computing platform. Experimental results have shown that applied multi-class classification method for Amazon product-review data using POS features has higher classification accuracy.

ANALYSIS OF IRIS AND PUPIL PARAMETERS FOR STRESS RECOGNITION

The aim of this study was to automatically identify the iris and pupil of the eye in the video stream and to parameterize the identified structures in order to make assumptions if the subjected is stressed or not. During tests subjects were given a number of issues which they had to respond by selecting only one correct answer. Visual material was gathered using a helmet-fitted stationary near-infrared camera that recorded iris and pupil of the eye reactions to stimuli. Subsequently it was made an automatic iris and pupil recognition and approximation by curves in the gathered sequence of images. Each change in the pupil size is described by different time series length. Thus, it is impossible to compare the obtained data using the Euclidean distance measures. For this reason, the metrics based on periodograms was used to compare the data series. The differences calculated between the eye pupil reaction to stimuli and question show-up time was introduced in multidimensional scaling algorithms for dimension reduction. It was noticed that the stimulus to the false answers tend to cluster. DOI: http://dx.doi.org/10.5755/j01.itc.41.1.1206

Cloud Computing Approach for Intelligent Visualization of Multidimensional Data

In this paper, a Cloud computing approach for intelligent visualization of multidimensional data is proposed. Intelligent visualization enables to create visualization models based on the best practices and experience. A new Cloud computing-based data mining system DAMIS is introduced for the intelligent data analysis including data visualization methods. It can assist researchers to handle large amounts of multidimensional data when executing resource-expensive and time-consuming data mining tasks by considerably reducing the information load. The application of DAMIS is illustrated by the visual analysis of medical streaming data.

Energy efficient platform for sobel filter implementation in energy and size constrained systems

Designing a high performance and energy efficient image processing solution for a very limited platform of a small UAV (Unmanned Air Vehicle) is very challenging. We address this issue by conducting a research of low power (under 10 Watt) and small sized (slightly larger than a credit card) embedded platforms with high performance computing capabilities. Sobel filter algorithm used in image processing will be benchmarked using different embedded platforms and frameworks of parallel computing to evaluate energy consumption and image processing performance, thus easing the design selections for a software engineer. The research results show, that Radxa Rock2 platform using Mali T764 GPU appeared to be 6.85x more energy efficient and 3.7x times better performing than Parallella platform using 16 core Epiphany co-processor when computing Sobel filter on 1080p resolution image.