COMPARATIVE ANALYSIS OF CLUSTERING METHODS SUITABLE FOR PLANT VARIETIES MORPHOLOGICAL CHARACTERISTICS DATA PROCESSING - Orlenko, N. S.; Mazhuha, K. M.; Dushar, M. B.; Maslechkin, V. V. (Scientific Progress & Innovations, 2019)
Despite the fact that clustering is uncontrolled classification of multi-dimensional data in corresponding clusters, the clustering problem has been addressed in many contexts and by researchers in many subjects. One of the research areas, where clustering is useful, is morphological analysis of plant variety characteristics, which helps to identify new varieties more accurately. That is why it is important to compare the results of clustering and the using of other methods and measure intervals in order to determine the most suitable methods for morphological characteristics analysis. The following methods were used during the research: analytical, mathematical, statistical, and graphic. This paper presents a comparative analysis of clustering methods using the famous Fisher’s Iris data set and also the classification methods, which are the most suitable for analyzing morphological characteristics of plant varieties. As a result, this paper presents a survey of better plant varieties clustering results influenced by different hierarchical agglomerative classification methods (Between-Groups Linkage, Within Groups, Nearest Neighbor, Furthest Neighbor, Centroid Method, Median Method, Wards Method) using Euclidean and non-Euclidean measure intervals. Clustering results were evaluated by using descriptive statistics methods (cross-tables). Some clustering algorithms and technologies, which we used during the research, were also described. The article considers possible measure interval which is used in algorithms, and presents the most popular clustering algorithms and shows their role in the Data mining. Numerous techniques and clustering algorithms were suggested earlier to assist clustering of time series data streams. The clustering algorithms and their effectiveness in various applications are compared to recognize the most suitable method to solve the existing problem of morphological analysis and new plant varieties identification. The best results were obtained using Average Linkage (Between Groups) with Pearson Correlation measure interval, Average Linkage (Within Group) with Cosine measure interval, Average Linkage (Within Group) with Pearson Correlation measure interval, Ward Method with Cosine measure interval. Frequency statistics (cross-tables) to evaluate the quality of classification results was suggested. Thus, the conducted testing proved that there is no universal algorithm that would ideally distribute the set of Fisher’s Irises to clusters. Therefore, clustering of plant varieties should be carried out iteratively, consistently applying the most common clustering algorithms and carefully evaluating clustering results in order to select the method and measure interval, which classify plant varieties most optimally and enable to interpret the classification results correctly.