Some Basic Characterization of the Function γ
Rena Eldar Kizi Kerbalayeva
Issue:
Volume 7, Issue 5, October 2021
Pages:
72-75
Received:
28 August 2021
Accepted:
23 September 2021
Published:
28 October 2021
Abstract: In this paper I have studied some characterization of the function γ. As in areas of Mathematics, we need a precise of given problem result in order to be absolutely clear. This paper seeks to do that and introduce new applications to aid our study. Some steps of the solutions to given paper in Basic Mathematics for the Analysis course involve arithmetic calculations that are too complicated to be performed mentally. In this paper I have included three Study Skills Checklists introduced to actively give how effectively use following views. The beginning of the paper has been introduced some properties of having sequences as a complete study this problem. In this instance, I have shown the actual computations that must be made to complete the formal prove. Hence than simply list the steps of arithmetic calculations making no mention of how the numerical values within the graphs are behaved, this unique feature will help answer often given question, from a interesting mathematics, “Is the function γ rational?” Since information is often presented in the form of graphs, I need to be able to give some characterizations of a function of a natural-number argument (a sequence) and natural logarithmic (Napierian logarithms) function displayed in this way. It also serves as a method for the Euler transformations that I can perform immediately to solve the problem in this paper. Henceforth according to l’Hopital’s rule one can easy to solve needing limit.
Abstract: In this paper I have studied some characterization of the function γ. As in areas of Mathematics, we need a precise of given problem result in order to be absolutely clear. This paper seeks to do that and introduce new applications to aid our study. Some steps of the solutions to given paper in Basic Mathematics for the Analysis course involve arit...
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A Comparison of K-Means and Mean Shift Algorithms
Issue:
Volume 7, Issue 5, October 2021
Pages:
76-84
Received:
25 August 2021
Accepted:
30 September 2021
Published:
27 November 2021
Abstract: Clustering, also known as cluster analysis, is a learning problem that occurs without the intervention of a human. This technique is frequently used very efficiently in data analysis to observe and identify interesting, useful, or desirable patterns in data. The clustering technique operates by dividing the data involved into similar objects based on their identified properties. This process results in the formation of groups, and each formed group is referred to as a cluster. A single said cluster consists of objects from the data that share similarities with other objects found in the same cluster and differ from objects identified from the data that now exist in other clusters. Clustering is an important process in many aspects of data analysis because it determines and presents the intrinsic grouping of objects in the data based on their attributes in a batch of unlabeled raw data. This method of cluster analysis lacks a textbook or, to put it another way, good criteria. This is due to the fact that this process is unique and customizable for each user who requires it for a variety of reasons. There is no single best clustering algorithm because it is so dependent on the user's scenario and needs. The purpose of this paper is to compare and contrast two different clustering algorithms. The algorithms under consideration are the k- mean and the mean shift. These algorithms are compared based on the following criteria: time complexity, training, prediction performance, and clustering algorithm accuracy.
Abstract: Clustering, also known as cluster analysis, is a learning problem that occurs without the intervention of a human. This technique is frequently used very efficiently in data analysis to observe and identify interesting, useful, or desirable patterns in data. The clustering technique operates by dividing the data involved into similar objects based ...
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