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A novel grid-based clustering algorithm

Starczewski Artur, Scherer Magdalena M., Książek Wojciech, Dębski Maciej, Wang Lipo

Journal of Artificial Intelligence and Soft Computing Research

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2021

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Vol. 11, No. 4

319--330

EN

Data clustering is an important method used to discover naturally occurring structures in datasets. One of the most popular approaches is the grid-based concept of clustering algorithms. This kind of method is characterized by a fast processing time and it can also discover clusters of arbitrary shapes in datasets. These properties allow these methods to be used in many different applications. Researchers have created many versions of the clustering method using the grid-based approach. However, the key issue is the right choice of the number of grid cells. This paper proposes a novel grid-based algorithm which uses a method for an automatic determining of the number of grid cells. This method is based on the kdist function which computes the distance between each element of a dataset and its kth nearest neighbor. Experimental results have been obtained for several different datasets and they confirm a very good performance of the newly proposed method.

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A new approach to detection of changes in multidimensional patterns. Part 2

Gałkowski Tomasz, Krzyżak Adam, Patora-Wysocka Zofia, Filutowicz Zbigniew, Wang Lipo

Journal of Artificial Intelligence and Soft Computing Research

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2021

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Vol. 11, No. 3

217--227

EN

In the paper we develop an algorithm based on the Parzen kernel estimate for detection of sudden changes in 3-dimensional shapes which happen along the edge curves. Such problems commonly arise in various areas of computer vision, e.g., in edge detection, bioinformatics and processing of satellite imagery. In many engineering problems abrupt change detection may help in fault protection e.g. the jump detection in functions describing the static and dynamic properties of the objects in mechanical systems. We developed an algorithm for detecting abrupt changes which is nonparametric in nature and utilizes Parzen regression estimates of multivariate functions and their derivatives. In tests we apply this method, particularly but not exclusively, to the functions of two variables.

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On training deep neural networks using a streaming approach

Duda Piotr, Jaworski Maciej, Cader Andrzej, Wang Lipo

Journal of Artificial Intelligence and Soft Computing Research

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2020

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Vol. 10, No. 1

15--26

EN

In recent years, many deep learning methods, allowed for a significant improvement of systems based on artificial intelligence methods. Their effectiveness results from an ability to analyze large labeled datasets. The price for such high accuracy is the long training time, necessary to process such large amounts of data. On the other hand, along with the increase in the number of collected data, the field of data stream analysis was developed. It enables to process data immediately, with no need to store them. In this work, we decided to take advantage of the benefits of data streaming in order to accelerate the training of deep neural networks. The work includes an analysis of two approaches to network learning, presented on the background of traditional stochastic and batch-based methods.

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A novel drift detection algorithm based on features’ importance analysis in a data streams environment

Duda Piotr, Przybyszewski Krzysztof, Wang Lipo

Journal of Artificial Intelligence and Soft Computing Research

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2020

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Vol. 10, No. 4

287--298

EN

The training set consists of many features that influence the classifier in different degrees. Choosing the most important features and rejecting those that do not carry relevant information is of great importance to the operating of the learned model. In the case of data streams, the importance of the features may additionally change over time. Such changes affect the performance of the classifier but can also be an important indicator of occurring concept-drift. In this work, we propose a new algorithm for data streams classification, called Random Forest with Features Importance (RFFI), which uses the measure of features importance as a drift detector. The RFFT algorithm implements solutions inspired by the Random Forest algorithm to the data stream scenarios. The proposed algorithm combines the ability of ensemble methods for handling slow changes in a data stream with a new method for detecting concept drift occurrence. The work contains an experimental analysis of the proposed algorithm, carried out on synthetic and real data.