A statistical approach for clustering in streaming data

Mozafari, Niloofar; Hashemi, Sattar; Hamzeh, Ali

doi:10.5430/air.v3n1p38

Cited by 6 publications

(4 citation statements)

References 47 publications

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“…Examples of this strategy have been reported previously. 3,5,[12][13][14][15] We refer to this second approach as online or incremental clustering. Algorithms for online clustering can themselves be divided into two subcategories.…”

Section: Background and Related Workmentioning

confidence: 99%

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Online cluster validity indices for performance monitoring of streaming data clustering

et al. 2018

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Cluster analysis is used to explore structure in unlabeled batch data sets in a wide range of applications. An important part of cluster analysis is validating the quality of computationally obtained clusters. A large number of different internal indices have been developed for validation in the offline setting. However, this concept cannot be directly extended to the online setting because streaming algorithms do not retain the data, nor maintain a partition of it, both needed by batch cluster validity indices. In this paper, we develop two incremental versions (with and without forgetting factors) of the Xie‐Beni and Davies‐Bouldin validity indices, and use them to monitor and control two streaming clustering algorithms (sk‐means and online ellipsoidal clustering), In this context, our new incremental validity indices are more accurately viewed as performance monitoring functions. We also show that incremental cluster validity indices can send a distress signal to online monitors when evolving structure leads an algorithm astray. Our numerical examples indicate that the incremental Xie‐Beni index with a forgetting factor is superior to the other three indices tested.

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Section: Background and Related Workmentioning

confidence: 99%

“…This strategy is espoused, for example, in [9], [10], [11]; (2) using incremental learning techniques to find clusters in the evolving data stream. Examples of this strategy include [3], [5], [12], [13], [14], [15]. We refer to this second approach as online or incremental clustering.…”

Section: Background and Related Workmentioning

confidence: 99%

Online cluster validity indices for performance monitoring of streaming data clustering

et al. 2018

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“…They maintain cluster "footprints" that summarize the clusters discovered and have a mechanism of incrementally updating those footprints as new vectors arrive. In [9]- [13], underlying probabilistic models are used and the footprint contains probability distribution parameters. Many of the density based streaming models also contain footprint entries that allow calculation of basic summary statistics [14]- [18].…”

Section: Introductionmentioning

confidence: 99%

StreamSoNG: A Soft Streaming Classification Approach

Wang

Keller

Dale

et al. 2022

IEEE Trans. Emerg. Top. Comput. Intell.

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Examining most streaming clustering algorithms leads to the understanding that they are actually incremental classification models. They model existing and newly discovered structures via summary information that we call footprints. Incoming data is normally assigned crisp labels (into one of the structures) and that structure's footprints are incrementally updated. There is no reason that these assignments need to be crisp. In this paper, we propose a new streaming classification algorithm that uses Neural Gas prototypes as footprints and produces a possibilistic label vector (typicalities) for each incoming vector. These typicalities are generated by a modified possibilistic k-nearest neighbor algorithm. The approach is tested on synthetic and real image datasets with excellent results.

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“…[50][17][51][52], underlying probabilistic models are used and the footprint contains probability distribution parameters. Many of the density-based streaming models also contain footprint entries that allow calculation of basic summary statistics[53][54][55][56][57].…”

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confidence: 99%

Early detection of health changes in the elderly using in-home multi-sensor data streams

Wang¹

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The rapid aging of the population worldwide requires increased attention from health care providers and the entire society. For the elderly to live independently, many health issues related to old age, such as frailty and risk of falling, need increased attention and monitoring. When monitoring daily routines for older adults, it is desirable to detect the early signs of health changes before serious health events, such as hospitalizations, happen, so that timely and adequate preventive care may be provided. By deploying multi-sensor systems in homes of the elderly, we can track trajectories of daily behaviors in a feature space defined using the sensor data. In this work, we investigate a methodology for learning data distribution from streaming data and tracking the evolution of the behavior trajectories over long periods (years) using high dimensional streaming clustering and provide very early indicators of changes in health. If we assume that habitual behaviors correspond to clusters in feature space and diseases produce a change in behavior, albeit not highly specific, tracking trajectory deviations can provide hints of early illness. Retrospectively, we visualize the streaming clustering results and track how the behavior clusters evolve in feature space with the help of two dimension-reduction algorithms, Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE). Moreover, our tracking algorithm in the original high dimensional feature space generates early health warning alerts if a negative trend is detected in the behavior trajectory. We validated our algorithm on synthetic data, real-world data and tested it on a pilot dataset of four TigerPlace residents monitored with a collection of motion, bed, and depth sensors over ten years. We used the TigerPlace electronic health records (EHR) to understand the residents' behavior patterns and to evaluate and explain the health warnings generated by our algorithm. The results obtained on the TigerPlace dataset show that most of the warnings produced by our algorithm can be linked to health events documented in the EHR, providing strong support for a prospective deployment of the approach.

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A statistical approach for clustering in streaming data

Cited by 6 publications

References 47 publications

Online cluster validity indices for performance monitoring of streaming data clustering

Online cluster validity indices for performance monitoring of streaming data clustering

StreamSoNG: A Soft Streaming Classification Approach

Early detection of health changes in the elderly using in-home multi-sensor data streams

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