SOTAVerified

A Framework for Clustering Uncertain Data

2015-08-01VLDB 2015Code Available0· sign in to hype

Erich Schubert, Alexander Koos, Tobias Emrich, Andreas Zufle, Klaus Arthur Schmid, Arthur Zimek

Code Available — Be the first to reproduce this paper.

Reproduce

Code

Abstract

The challenges associated with handling uncertain data, in particular with querying and mining, are finding increasing attention in the research community. Here we focus on clustering uncertain data and describe a general framework for this purpose that also allows to visualize and understand the impact of uncertainty—using different uncertainty models—on the data mining results. Our framework constitutes release 0.7 of ELKI (http://elki.dbs.ifi.lmu.de/) and thus comes along with a plethora of implementations of algorithms, distance measures, indexing techniques, evaluation measures and visualization components.

Tasks

Reproductions