A framework for clustering uncertain data

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

doi:10.14778/2824032.2824115

Cited by 131 publications

(68 citation statements)

References 22 publications

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“…The final result was a dataset with 50000 examples, 1508 of which were designated as outliers, and with a dimensionality of 27. We use outlier scores generated by the ELKI software [17] consisting of kNN, weighted kNN, LOF, Simplied-LOF, LDOF and LoOP run with k in the range of 3, 5, 7 . .…”

Section: B Aloi Outlier Datasetmentioning

confidence: 99%

Anomaly Detection Ensembles: In Defense of the Average

Chiang

Yeh

2015

2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

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When given multiple models it is often useful to combine them for improved reliability or performance over the individual models. Over the years many outlier metrics and detection methods have been developed for the purposed of finding data incongruous with the rest of the data. Inspired by the successes of supervised ensemble machine learning, many have proposed combining multiple anomaly detection methods together. We investigate the usefulness of building ensembles for the purpose of anomaly detection. We find that currently, to the best of our knowledge, there is no great advantage in using anything more complicated than the simple average over all available outlier scores.

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Section: B Aloi Outlier Datasetmentioning

confidence: 99%

Anomaly Detection Ensembles: In Defense of the Average

Chiang

Yeh

2015

2015 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

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“…RTreeIndex.Update(S) (Schubert et al, 2015), to implement the OPTICS clustering algorithm. Following we discuss the results in each of the three phases.…”

Section: Resultsmentioning

confidence: 99%

Geometry of interest (GOI): spatio-temporal destination extraction and partitioning in GPS trajectory data

Mousavi

Harwood

Karunasekera

et al. 2016

J Ambient Intell Human Comput

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Nowadays large amounts of GPS trajectory data is being continuously collected by GPS-enabled devices such as vehicles navigation systems and mobile phones. GPS trajectory data is useful for applications such as traffic management, location forecasting, and itinerary planning. Such applications often need to extract the time-stamped Sequence of Visited Locations (SVLs) of the mobile objects. The nearest neighbor query (NNQ) is the most applied method for labeling the visited locations based on the IDs of the POIs in the process of SVL generation. NNQ in some scenarios is not accurate enough. To improve the quality of the extracted SVLs, instead of using NNQ, we label the visited locations as the IDs of the POIs which geometrically intersect with the GPS observations. Intersection operator requires the accurate geometry of the points of interest which we refer to them as the Geometries

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“…The accuracy of clustering is 99.48%. Clustering algorithms from open source package ELKI [22] could not correctly separate all 5 classes. The best results (Fig.…”

Section:  mentioning

confidence: 99%

Computationally efficient methods of clustering ensemble construction for satellite image segmentation

Пестунов¹,

Rylov²,

Sinyavskiy³

et al. 2017

Collection of Selected Papers of the III International Conference on Information Technology and Nanotechnology

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Combining multiple partitions into single ensemble clustering solution is a prominent way to improve accuracy and stability of clustering solutions. One of the major problems in constructing clustering ensembles is high computational complexity of the common methods. In this paper two computationally efficient methods of constructing ensembles of nonparametric clustering algorithms are introduced. They are based on the use of co-association matrix and subclusters. The results of experiments on synthetic and real datasets confirm their effectiveness and show the stability of the obtained solutions. The performance of the proposed methods allows to process large images including multispectral satellite data.

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A framework for clustering uncertain data

Cited by 131 publications

References 22 publications

Anomaly Detection Ensembles: In Defense of the Average

Anomaly Detection Ensembles: In Defense of the Average

Geometry of interest (GOI): spatio-temporal destination extraction and partitioning in GPS trajectory data

Computationally efficient methods of clustering ensemble construction for satellite image segmentation

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