The ClusTree: indexing micro-clusters for anytime stream mining

Kranen, Philipp; Assent, Ira; Baldauf, Corinna; Seidl, Thomas

doi:10.1007/s10115-010-0342-8

Cited by 230 publications

(165 citation statements)

References 23 publications

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“…Since this abundant -however raw -data do not provide interesting behavior patterns, data mining techniques, especially inductive learning, have been applied to extract useful knowledge from this type of data [13,10,21,6]. Extracting patterns from data streams and their usage in real-time is an effervescent research topic that has been tackled during the last decades [14,27,4,15,28].…”

Section: Learning From Data Streamsmentioning

confidence: 99%

On Dynamic Feature Weighting for Feature Drifting Data Streams

Barddal

Gomes

Enembreck

et al. 2016

Machine Learning and Knowledge Discovery in Databases

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Section: Learning From Data Streamsmentioning

confidence: 99%

On Dynamic Feature Weighting for Feature Drifting Data Streams

Barddal

Gomes

Enembreck

et al. 2016

Machine Learning and Knowledge Discovery in Databases

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“…ClusTree [32] is a parameter-free approach that maintains stream summaries, and adapts itself to different stream speeds.…”

Section: Micro-clusteringmentioning

confidence: 99%

Distributed Gaussian Mixture Model Summarization Using the MapReduce Framework

Esmaeilpour

Bigdeli

Cheraghchi

et al. 2016

Advances in Artificial Intelligence

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With an accelerating rate of data generation, sophisticated techniques are essential to meet scalability requirements. One of the promising avenues for handling large datasets is distributed storage and processing. Hadoop is a well-known framework for distributed storage and processing. Further, data summarization is a useful concept for managing large datasets. Data summarization techniques are intended to produce compact yet representative summaries for the entire dataset.Consolidation of these tools can allow a distributed implementation of data summarization. In this thesis, this goal is achieved by proposing and implementing a distributed Gaussian Mixture Model Summarization using the MapReduce framework (MR-SGMM). The main purpose of the proposed method is to summarize a dataset with a density-based clustering algorithm called DBSCAN algorithm, and then summarize each discovered cluster using the SGMM approach in a distributed manner. Testing the implementation with synthetic and real datasets is used to demonstrate its validity and efficiency.ii

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“…An extreme condition of this resource-scarced case is the clustering of data streams, where samples must be processed in chunks only once, as they arrive, without being able to access them again. This problem is well understood today, and there are several techniques to solve it such as [1,2,16,26,36,64].…”

Section: Related Workmentioning

confidence: 99%

Warped K-Means: An algorithm to cluster sequentially-distributed data

Leiva

Vidal

2013

Information Sciences

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Many devices generate large amounts of data that follow some sort of sequentiality, e.g., motion sensors, e-pens, eye trackers, etc. and often these data need to be compressed for classification, storage, and/or retrieval tasks. Traditional clustering algorithms can be used for this purpose, but unfortunately they do not cope with the sequential information implicitly embedded in such data. Thus, we revisit the wellknown K-means algorithm and provide a general method to properly cluster sequentially-distributed data. We present Warped K-Means (WKM), a multi-purpose partitional clustering procedure that minimizes the Sum of Squared Error criterion, while imposing a hard sequentiality constraint in the classification step. We illustrate the properties of WKM in three applications, one being the segmentation and classification of human activity. WKM outperformed five state-of-the-art clustering techniques to simplify data trajectories, achieving a recognition accuracy of near 97%, which is an improvement of around 66% over their peers. Moreover, such an improvement came with a reduction in the computational cost of more than one order of magnitude.

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The ClusTree: indexing micro-clusters for anytime stream mining

Cited by 230 publications

References 23 publications

On Dynamic Feature Weighting for Feature Drifting Data Streams

On Dynamic Feature Weighting for Feature Drifting Data Streams

Distributed Gaussian Mixture Model Summarization Using the MapReduce Framework

Warped K-Means: An algorithm to cluster sequentially-distributed data

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