A Rapid Review of Clustering Algorithms

Clustering algorithms aim to organize data into groups or clusters based on the inherent patterns and similarities within the data. They play an important role in today's life, such as in marketing and e-commerce, healthcare, data organization and analysis, and social media. Numerous clustering algorithms exist, with ongoing developments introducing new ones. Each algorithm possesses its own set of strengths and weaknesses, and as of now, there is no universally applicable algorithm for all tasks. In this work, we analyzed existing clustering algorithms and classify mainstream algorithms across five different dimensions: underlying principles and characteristics, data point assignment to clusters, dataset capacity, predefined cluster numbers and application area. This classification facilitates researchers in understanding clustering algorithms from various perspectives and helps them identify algorithms suitable for solving specific tasks. Finally, we discussed the current trends and potential future directions in clustering algorithms. We also identified and discussed open challenges and unresolved issues in the field.Comment: 25 pages, 7 figures, 3 table

Система рекомендації лотів аукціону

Проект містить: 75 с., 1 табл., 28 рис., 21 посилання на джерела. Об’єктом розробки є система рекомендації лотів аукціону. Мета розробки – побудова моделі для прогнозування покупок користувача аукціону та надання рекомендацій, опираючись на історію переглядів та покупок. У дипломному проекті розроблено систему рекомендації лотів аукціону. У ході роботи над проектом було розглянуто основні методи створення рекомендацій та можливість їх використання в рамках інтернет-аукціону. За допомогою алгоритмів кластерного аналізу та статистичних методів було побудовано модель на основі графу для прогнозування покупок користувача та генерації рекомендацій, що опирається на історію переглядів та покупок. Побудована модель дозволяє полегшити процес пошуку лотів для користувача та збільшити його лояльність до платформи з першого відвідування, що сприяє зростанню кількості проданих лотів аукціону.The project contains 75 p., 1 table, 28 figures, 21 references to the source. The object of development is recommendation system for auction lots. The purpose of the development is to develop a model for forecasting auction user purchases and providing recommendations, based on the history of views and purchases. The graduation project has developed a system of recommendations for auction lots. The main methods of creating recommendations and the possibility of their use in the framework of the Internet auction were considered during the work on the project. A graph-based model for predicting user purchases and generating recommendations based on the history of views and purchases was developed, using cluster analysis algorithms and statistical methods. The developed model helps to simplify the lots search process for the auctions customers and increase their loyalty to the platform from the first visit, which contributes to the increase in the number of auctioned lots soldВ дипломном проекте разработано систему рекомендации лотов аукциона. Построеная модель позволяет упростить процесс поиска лотов для пользователя с первого посещения, что способствует росту количества проданных лотов аукцион

Vineyard zonation based on natural terroir factors using multivariate statistics – Case study Burgenland (Austria)

Aim: The aim of the study was to explicitly develop a methodology of delineation of natural Protected Designation of Origins (PDO) terroir regions for Austria, where PDO viticulture regions reflect natural conditions only in a formal manner. Methods and results: There is increasing competition in the wine market from globalized trends, where the European Union (EU) and non-EU wine producers have adopted different market strategies to promote their wines and gain larger market share. The EU has therefore established protective agricultural product categories such as PDO and Protected Geographical Indication (PGI) based on the terroir concept which contrasts with USA strategies. Here, we first collected and derived as many as possible relevant physical geographical data for a total of 66,673 officially registered vineyard areas in Burgenland (Austria). Next, we applied factor analysis to these data with the aim to shrink their size and reduce their dimensionality. For each vineyard plot was derived a factor score which was used for performing k-means clustering. The best count of clusters, k-parameter, was estimated using five internal validity indices. Five homogenous management zones were created as a result of clustering. Correctness and accuracy of the clustering was evaluated by multidimensional discriminant analysis. The final zones were compared to current Districtus Austriae Controllatus (DAC) of Burgenland. Conclusion: It was found by the comparison of DAC regions of Burgenland and our drafted zones that some of the DAC regions do not respect natural terroir zones, while these regions were created as PDO regions which should respect their natural terroir. Significance and impact of the study: The presented methodology can be applied all over Austria and, with some modifications caused by different input data, to each EU member country where it is necessary to revise PDO regions’ borders