Probability κ-means clustering for neural network architecture

研究成果: Conference contribution同行評審

4 引文 斯高帕斯(Scopus)

摘要

Cluster analysis indices are aimed at classifying the elements used to estimate the quality of the categories based on their similarity. It is a challenging task because, with the same data set, there may be many partitions that fit natural groupings of a given data set. Applications of clustering can include pattern recognition, image analysis and information retrieval. We propose an approach based on the κ-means concept that clustering centers more often have a higher density than their neighbors: then we use a probability κ-means algorithm to achieve fuzzy clustering in continuous form over a relatively large distance from other points with higher densities. Further, in order to follow the mainstream neural network architecture, we define a favorable activation function and corresponding loss function for the clustering iteration. Our method come from the basis of a clustering procedure in which the number of clusters arises intuitively and clusters are achieved regardless of the high dimensions. We demonstrate the result of complete algorithm on several clustering test cases.

原文English
主出版物標題ICAAI 2019 - 2019 the 3rd International Conference on Advances in Artificial Intelligence
發行者Association for Computing Machinery
頁面52-57
頁數6
ISBN(電子)9781450372534
DOIs
出版狀態Published - 26 10月 2019
事件3rd International Conference on Advances in Artificial Intelligence, ICAAI 2019 - Istanbul, Turkey
持續時間: 26 10月 201928 10月 2019

出版系列

名字ACM International Conference Proceeding Series

Conference

Conference3rd International Conference on Advances in Artificial Intelligence, ICAAI 2019
國家/地區Turkey
城市Istanbul
期間26/10/1928/10/19

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