Semi-supervised Clustering Framework Based on Active Learning for Real Data

Autor:	Odate Ryosuke, Masahiro Motobayashi, Suzuki Yasufumi, Hiroshi Shinjo
Rok vydání:	2018
Předmět:	Set (abstract data type) ComputingMethodologies_PATTERNRECOGNITION Constant (computer programming) Active learning (machine learning) Computer science Outlier Data mining Cluster analysis Focus (optics) computer.software_genre computer Ward's method Semi supervised clustering
Zdroj:	Lecture Notes in Computer Science ISBN: 9783319977843 S+SSPR
DOI:	10.1007/978-3-319-97785-0_18
Popis:	In this paper, we propose a real data clustering method based on active learning. Clustering methods are difficult to apply to real data for two reasons. First, real data may include outliers that adversely affect clustering. Second, the clustering parameters such as the number of clusters cannot be made constant because the number of classes of real data may increase as time goes by. To solve the first problem, we focus on labeling outliers. Therefore, we develop a stream-based active learning framework for clustering. The active learning framework enables us to label the outliers intensively. To solve the second problem, we also develop an algorithm to automatically set clustering parameters. This algorithm can automatically set the clustering parameters with some labeled samples. The experimental results show that our method can deal with the problems mentioned above better than the conventional clustering methods.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::1977f4311d9b64e8dc32208ad1b9d6c0 https://doi.org/10.1007/978-3-319-97785-0_18 Zobrazit plný text záznamu