The aim of this work is to get basic overview about the process of obtaining knowledge from databases - datamining and to analyze the datamining system developed at FIT BUT on the NetBeans platform in order to create a new mining module. We decided to implement a module for mining outliers and to extend existing regression module with multiple linear regression using generalized linear models. New methods using existing methods of Oracle Data Mining.

Data collecting and analysis are commonly used techniques in many sectors of today's business and science. Process called Knowledge Discovery in Databases presents itself as a great tool to find new and interesting information that can be used in a future developement. This thesis deals with basic principles of data mining and temporal data mining as well as with specifics of concrete implementation of chosen algorithms for mining periodic patterns in time series. These algorithms have been developed in a form of managed plug-ins for Microsoft Analysis Services -- service that provides data mining features for Microsoft SQL Server. Finally, we discuss obtained results of performed experiments focused on time complexity of implemented algorithms.

Frequent-pattern mining from databases has been widely studied and frequently observed. Unfortunately, these algorithms are not suitable for data stream processing. In frequent-pattern mining from data streams, it is important to manage sets of items and also their history. There are several reasons for this; it is not just the history of frequent items, but also the history of potentially frequent sets that can become frequent later. This requires more memory and computational power. This thesis describes two algorithms: Lossy Counting and FP-stream. An effective implementation of these algorithms in C# is an integral part of this thesis. In addition, the two algorithms have been compared.

This master's thesis is focused on knowledge discovery from databases, especially on methods of mining sequential patterns. Individual methods of mining sequential patterns are described in detail. Further, this work deals with extending the platform Microsoft SQL Server Analysis Services of new mining algorithms. In the practical part of this thesis, plugins for mining sequential patterns are implemented into MS SQL Server. In the last part, these algorithms are compared on different data sets.

This master's thesis deals with with FIT-Miner, the system for knowledge discovery in databases. The first part of this paper describes the data-mining process, mixture model's issues and FIT-Miner system. Second part deals with design, implementation and testing of created module, which is used for cluster analysis with Expectation-Maximalization algorithm. The end of the paper is focused to design of modules using Java Store Procedures Technology.

This master's thesis deals with development of a module for a data mining system, which is being developed on FIT. The first part describes the general knowledge discovery process and cluster analysis including cluster validation; it also describes Oracle Data Mining including algorithms, which it uses for clustering. At the end it deals with the system and the technologies it uses, such as NetBeans Platform and DMSL. The second part describes design of a clustering module and a module used to compare its results. It also deals with visualization of cluster analysis results and shows the achievements.

This thesis is focused on knowledge discovery from databases, especially on methods of classification and prediction. These methods are described in detail.  Furthermore, this work deals with multimedia databases and the way these databases store data. In particular, the method for processing low-level image and video data is described.  The practical part of the thesis focuses on the implementation of this GMM method used for extracting low-level features of video data and images. In other parts, input data and tools, which the implemented method was compared with, are described.  The last section focuses on experiments comparing extraction efficiency features of high-level attributes of low-level data and the methods implemented in selected classification tools LibSVM.

This thesis deals with the design and implementation of a cluster analysis module for currently developing datamining system DataMiner on FIT BUT. So far, the system lacked cluster analysis module. The main objective of the thesis was therefore to extend the system of such a module. Together with me, Pavel Riedl worked on the module. We have created a common part for all the algorithms so that the system can be easily extended to other clustering algorithms. In the second part, I extended the clustering module by adding three density based clustering aglorithms - DBSCAN, OPTICS and DENCLUE. Algorithms have been implemented and appropriate sample data was chosen to verify theirs functionality.

The subject of this master's thesis is extension of existing data mining system. System will be extended by the module for the time series data mining. This thesis consists of common introduction to data mining issues and continues with time series analysis. Thesis then also contains some of the current tasks and algorithms used in time series data mining, follows by the concept of the implementation and description of the choosen mining method. Possible future system's improvments are disscused at the end of the paper.

<p>Skin lesion can be deadliest if not detected early. Early detection of skin lesion can save many lives. Artificial Intelligence and Machine learning is helping healthcare in many ways and so in the diagnosis of skin lesion. Computer aided diagnosis help clinicians in detecting the cancer. The study was conducted to classify the seven classes of skin lesion using very powerful convolutional neural networks. The two pre trained models i.e., DenseNet and Incepton-v3 were employed to train the model and accuracy, precision, recall, f1score and ROC-AUC was calculated for every class prediction. Moreover, gradient class activation maps were also used to aid the clinicians in determining what are the regions of image that influence model to make a certain decision. These visualizations are used for explainability of the model. Experiments showed that DenseNet performed better then Inception V3. Also it was noted that gradient class activation maps highlighted different regions for predicting same class. The main contribution was to introduce medical aided visualizations in lesion classification model that will help clinicians in understanding the decisions of the model. It will enhance the reliability of the model. Also, different optimizers were employed with both models to compare the accuracies.</p>