Ensemble Clustering for Novelty Detection in Data Streams

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

Kemilly Dearo Garcia (Author) Other; Elaine Ribeiro de Faria (Author) Other; Cláudio Rebelo de Sá (Author) Other; João Mendes Moreira (Author) FEUP; Charu C. Aggarwal (Author) Other; André C. P. L. F. de Carvalho (Author) Other; Joost N. Kok (Author) Other

Conference proceedings International

Title: Discovery Science - 22nd International Conference, DS 2019, Split, Croatia, October 28-30, 2019, Proceedings

Pages: 460-470

22nd International Conference on Discovery Science, DS 2019

28 October 2019 through 30 October 2019

Indexing

Scopus - 2 Citations

Other information

Authenticus ID: P-00R-FH2

DOI: 10.1007/978-3-030-33778-0_34

Abstract (EN): In data streams new classes can appear over time due to changes in the data statistical distribution. Consequently, models can become outdated, which requires the use of incremental learning algorithms capable of detecting and learning the changes over time. However, when a single classification model is used for novelty detection, there is a risk that its bias may not be suitable for new data distributions. A solution could be the combination of several models into an ensemble. Besides, because models can only be updated when labeled data arrives, we propose two unsupervised ensemble approaches: one combining clustering partitions using the same clustering technique; and other using different clustering techniques. We compare the performance of the proposed methods with well known novelty detection algorithms. The methods were tested on datasets commonly used in the novelty detection literature. The experimental results show that proposed ensembles have competitive performance for novelty detection in data streams. © Springer Nature Switzerland AG 2019.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11