Adaptive Nonparametric Clustering

Arbeitspapier

This paper presents a new approach to non-parametric cluster analysis called Adaptive Weights Clustering (AWC). The idea is to identify the clustering structure by checking at different points and for dierent scales on departure from local homogeneity. The proposed procedure describes the clustering structure in terms of weights wij each of them measures the degree of local inhomogeneity for two neighbor local clusters using statistical tests of "no gap" between them. The procedure starts from very local scale, then the parameter of locality grows by some factor at each step. The method is fully adaptive and does not require to specify the number of clusters or their structure. The clustering results are not sensitive to noise and outliers, the procedure is able to recover dierent clusters with sharp edges or manifold structure. The method is scalable and computationally feasible. An intensive numerical study shows a state-of-the-art performance of the method in various articial examples and applications to text data. Our theoretical study states optimal sensitivity of AWC to local inhomogeneity.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IRTG 1792 Discussion Paper ; No. 2018-018

Classification and Topics

• Classification: Wirtschaft ; Mathematical and Quantitative Methods: General
• Subject: adaptive weights ; clustering ; gap coecient ; manifold clustering

Contributors, Location and Time

• Event: Geistige Schöpfung
• (who): Efimov, Kirill ; Adamyan, Larisa ; Spokoiny, Vladimir
• Event: Veröffentlichung
• (who): Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
• (where): Berlin
• (when): 2018

Further information

• Handle: http://hdl.handle.net/10419/230729
• Last update: 10.03.2025, 11:44 AM CET