title: ÐÐµÑÐ¾Ð´ Ð³Ð»ÑÐ±Ð¾ÐºÐ¾Ð¹ ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð´Ð°Ð½Ð½ÑÑ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹ Ð¿Ð»Ð°Ð½ÐºÑÐ¾Ð½Ð°
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
Creators: name: ÐÐ±ÑÐ°ÑÐ¸Ð¼, ÐÐ°ÑÐº
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: ÐÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÐ¹ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑ
ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð·ÑÐµÐ½Ð¸Ðµ
Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ Ð¾Ð±ÑÐ°Ð·Ð¾Ð²
ÑÐ²ÐµÑÑÐ¾ÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ñ
Ð²ÑÐ´ÐµÐ»ÐµÐ½Ð¸Ðµ Ð¿ÑÐ¸Ð·Ð½Ð°ÐºÐ¾Ð²

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description: Advancements in automated imaging has made it possible to augment the data both in terms of quantity and quality. This has prompted the development of planktonic systems for acquiring the species level information of the communities. However, screening the huge amount of data has been a challenge for both humans and computers alike. This Masterâs thesis project is focusing on visual clustering of plankton image data, by implementing and applying state-of-the-art clustering methods on plankton image data sets. These data set were collected from the Baltic Sea using an imaging flow cytometer. In order to form the clusters, first the features were extracted using AlexNet and ResNet-18 CNNs. The resultant of each CNN was clustered using Hierarchical and K-means algorithm and evaluated by checking the purity level of each clustering method. The result shows that for a small number of classes the K-means method based on features extracted using ResNet-18 has the highest level of purity, however the Hierarchical method shows higher purity in case the number of clusters is low. On the other hand, the Hierarchical method shows better purity measures when the number of classes is huge.